1. The document discusses Convolutional Neural Networks (CNNs) for object recognition and scene understanding. It covers the biological inspiration from the human visual cortex, classical computer vision techniques, and the foundations of CNNs including LeNet and learning visual features.
2. CNNs apply successive layers of convolutions, nonlinear activations, and pooling to learn hierarchical representations of images. Modern CNN architectures have millions of parameters and dozens of layers to learn increasingly complex features.
3. CNNs have countless applications in areas like image classification, segmentation, detection, generation, and more due to their general architecture for learning spatial hierarchies of features from data.
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Explores the type of structure learned by Convolutional Neural Networks, the applications where they're most valuable and a number of appropriate mental models for understanding deep learning.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
A convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has Successfully been applied to analyzing visual imagery
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Explores the type of structure learned by Convolutional Neural Networks, the applications where they're most valuable and a number of appropriate mental models for understanding deep learning.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
A convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has Successfully been applied to analyzing visual imagery
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
ResNet (short for Residual Network) is a deep neural network architecture that has achieved significant advancements in image recognition tasks. It was introduced by Kaiming He et al. in 2015.
The key innovation of ResNet is the use of residual connections, or skip connections, that enable the network to learn residual mappings instead of directly learning the desired underlying mappings. This addresses the problem of vanishing gradients that commonly occurs in very deep neural networks.
In a ResNet, the input data flows through a series of residual blocks. Each residual block consists of several convolutional layers followed by batch normalization and rectified linear unit (ReLU) activations. The original input to a residual block is passed through the block and added to the output of the block, creating a shortcut connection. This addition operation allows the network to learn residual mappings by computing the difference between the input and the output.
By using residual connections, the gradients can propagate more effectively through the network, enabling the training of deeper models. This enables the construction of extremely deep ResNet architectures with hundreds of layers, such as ResNet-101 or ResNet-152, while still maintaining good performance.
ResNet has become a widely adopted architecture in various computer vision tasks, including image classification, object detection, and image segmentation. Its ability to train very deep networks effectively has made it a fundamental building block in the field of deep learning.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
ResNet (short for Residual Network) is a deep neural network architecture that has achieved significant advancements in image recognition tasks. It was introduced by Kaiming He et al. in 2015.
The key innovation of ResNet is the use of residual connections, or skip connections, that enable the network to learn residual mappings instead of directly learning the desired underlying mappings. This addresses the problem of vanishing gradients that commonly occurs in very deep neural networks.
In a ResNet, the input data flows through a series of residual blocks. Each residual block consists of several convolutional layers followed by batch normalization and rectified linear unit (ReLU) activations. The original input to a residual block is passed through the block and added to the output of the block, creating a shortcut connection. This addition operation allows the network to learn residual mappings by computing the difference between the input and the output.
By using residual connections, the gradients can propagate more effectively through the network, enabling the training of deeper models. This enables the construction of extremely deep ResNet architectures with hundreds of layers, such as ResNet-101 or ResNet-152, while still maintaining good performance.
ResNet has become a widely adopted architecture in various computer vision tasks, including image classification, object detection, and image segmentation. Its ability to train very deep networks effectively has made it a fundamental building block in the field of deep learning.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
This covers a end-to-end coverage of neural networks,CNN internals , Tensorflow and Keras basic , intution on object detection and face recognition and AI on Android x86.
Scene classification using Convolutional Neural Networks - Jayani WithanawasamWithTheBest
Scene Classification is used in Convolutional Neural Networks (CNNs). We seek to redefine computer vision as an AI problem, understand the importance of scene classification as well as challenges, and the difference between traditional machine learning and deep learning. Additionally, we discuss CNNs, using caffe for implementing CNNs and importact reosources to imorove.
CNNs
Jayani Withanawasam
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
1. 6.874, 6.802, 20.390, 20.490, HST.506
Computational Systems Biology
Deep Learning in the Life Sciences
Lecture 3:
Convolutional Neural Networks
Prof. Manolis Kellis
http://mit6874.github.io
Slides credit: 6.S191, Dana Erlich, Param Vir Singh,
David Gifford, Alexander Amini, Ava Soleimany
2. Today: Convolutional Neural Networks (CNNs)
1. Scene understanding and object recognition for machines (and humans)
– Scene/object recognition challenge. Illusions reveal primitives, conflicting info
– Human neurons/circuits. Visual cortex layers==abstraction. General cognition
2. Classical machine vision foundations: features, scenes, filters, convolution
– Spatial structure primitives: edge detectors & other filters, feature recognition
– Convolution: basics, padding, stride, object recognition, architectures
3. CNN foundations: LeNet, de novo feature learning, parameter sharing
– Key ideas: learn features, hierarchy, re-use parameters, back-prop filter learning
– CNN formalization: representations(Conv+ReLU+Pool)*N layers + Fully-connected
4. Modern CNN architectures: millions of parameters, dozens of layers
– Feature invariance is hard: apply perturbations, learn for each variation
– ImageNet progression of best performers
– AlexNet: First top performer CNN, 60M parameters (from 60k in LeNet-5), ReLU
– VGGNet: simpler but deeper (819 layers), 140M parameters, ensembles
– GoogleNet: new primitive=inception module, 5M params, no FC, efficiency
– ResNet: 152 layers, vanishing gradients fit residuals to enable learning
5. Countless applications: General architecture, enormous power
– Semantic segmentation, facial detection/recognition, self-driving, image
colorization, optimizing pictures/scenes, up-scaling, medicine, biology, genomics
3. 1a. What do you see, and how?
Can we teach machines to see?
6. What computers ‘see’: Images as Numbers
What the computer "sees"
Levin
Image
Processing
&
Computer
Vision
An image is just a matrix of numbers [0,255].i.e.,1080x1080x3 for an RGB image.
Question: is this Lincoln?Washington? Jefferson? Obama?
How can the computer answer this question?
What you see
Input Image Input Image + values Pixel intensity values
(“pix-el”=picture-element)
What you both see
Can I just do classification on the 1,166400-long image vector directly?
No. Instead: exploit image spatial structure. Learn patches. Build them up
8. Inspiration: human/animal visual cortex
• Layers of neurons: pixels, edges, shapes, primitives, scenes
• E.g. Layer 4 responds to bands w/ given slant, contrasting edges
9. Primitives: Neurons & action potentials
•Chemical accumulation across
dendritic connections
•Pre-synaptic axon
post-synaptic dendrite
neuronal cell body
•Each neuron receives multiple
signals from its many dendrites
•When threshold crossed, it fires
•Its axon then sends outgoing
signal to downstream neurons
•Weak stimuli ignored
•Sufficiently strong cross
activation threshold
•Non-linearity within
each neuronal level
•Neurons connected into circuits (neural networks): emergent properties, learning, memory
•Simple primitives arranged in simple, repetitive, and extremely large networks
•86 billion neurons, each connects to 10k neurons, 1 quadrillion (1012) connections
10. Abstraction layers: edges, bars, dir., shapes, objects, scenes
LGN: Small dots
V1: Orientation,
disparity, some color
V4: Color, basic shapes,
2D/3D, curvature
VTC: Complex features
and objects(VTC: ventral temporal cortex
•Abstraction layers visual cortex layers
•Complex concepts from simple parts, hierarchy
•Primitives of visual concepts encoded in
neuronal connection in early cortical layers
11. • Massive recent expanse of human brain has re-used a
relatively simple but general learning architecture
General “learning machine”, reused widely
• Hearing, taste, smell, sight, touch all re-
use similar learning architecture
Motor Cortex
Visual Cortex • Interchangeable
circuitry
• Auditory cortex
learns to ‘see’ if
sent visual signals
• Injury area tasks
shift to uninjured
areas
• Not fully-general learning, but well-adapted to our world
• Humans co-opted this circuitry to many new applications
• Modern tasks accessible to any homo sapiens (<70k years)
• ML primitives not too different from animals: more to come?
human
chimp
Hardware
expansion
12. Today: Convolutional Neural Networks (CNNs)
1. Scene understanding and object recognition for machines (and humans)
– Scene/object recognition challenge. Illusions reveal primitives, conflicting info
– Human neurons/circuits. Visual cortex layers==abstraction. General cognition
2. Classical machine vision foundations: features, scenes, filters, convolution
– Spatial structure primitives: edge detectors & other filters, feature recognition
– Convolution: basics, padding, stride, object recognition, architectures
3. CNN foundations: LeNet, de novo feature learning, parameter sharing
– Key ideas: learn features, hierarchy, re-use parameters, back-prop filter learning
– CNN formalization: representations(Conv+ReLU+Pool)*N layers + Fully-connected
4. Modern CNN architectures: millions of parameters, dozens of layers
– Feature invariance is hard: apply perturbations, learn for each variation
– ImageNet progression of best performers
– AlexNet: First top performer CNN, 60M parameters (from 60k in LeNet-5), ReLU
– VGGNet: simpler but deeper (819 layers), 140M parameters, ensembles
– GoogleNet: new primitive=inception module, 5M params, no FC, efficiency
– ResNet: 152 layers, vanishing gradients fit residuals to enable learning
5. Countless applications: General architecture, enormous power
– Semantic segmentation, facial detection/recognition, self-driving, image
colorization, optimizing pictures/scenes, up-scaling, medicine, biology, genomics
14. Using Spatial Structure
Idea: connect
patches of input to
neurons in hidden
layer.
Neuron connected
to region of input.
Only “sees”these
values.
Input: 2D
image.
Array of pixel
values
15. Using Spatial Structure
Connect patch in input layer to a single neuron in subsequent layer.
Use a sliding window to define connections.
How can we weight the patch to detect particular features?
16. Feature Extraction with Convolution
- Filter of size 4x4 :16 different weights
- Apply this same filter to 4x4 patches in input
- Shift by 2 pixels for next patch
This“patchy” operation isconvolution
1) Apply a set of weights – a filter – to extract local features
2) Use multiple filters to extract different features
3) Spatially share parameters of each filter
17. Fully Connected Neural Network
Fully Connected:
• Each neuron in
hidden layer
connected to all
neurons in input
layer
• No spatial information
• Many, many
parameters
Input:
• 2D image
• Vector of pixel
values
Key idea: Use spatial structure in input to inform architecture
of the network
18. High Level Feature Detection
Let’s identify key features in each image category
Wheels,License Plate,
Headlights
Door,Windows,Steps
Nose,Eyes,Mouth
25. X or X?
Image is represented as matrix of pixel values… and computers are literal!
We want to be able to classify an X as an X even if it’s shifted,shrunk,rotated, deformed.
Rohrer How do CNNs work?
27. (Goodfellow 2016)
Zero Padding Controls Output Size
• Full convolution: zero pad input so output is produced whenever an output value
contains at least one input value (expands output)
• Valid-only convolution: output only when
entire kernel contained in input (shrinks output)
• Same convolution: zero pad input so output
is same size as input dimensions
x = tf.nn.conv2d(x, W, strides=[1,strides,strides,1],padding='SAME')
• TF convolution operator takes stride and zero fill option as parameters
• Stride is distance between kernel applications in each dimension
• Padding can be SAME or VALID
28.
29. Today: Convolutional Neural Networks (CNNs)
1. Scene understanding and object recognition for machines (and humans)
– Scene/object recognition challenge. Illusions reveal primitives, conflicting info
– Human neurons/circuits. Visual cortex layers==abstraction. General cognition
2. Classical machine vision foundations: features, scenes, filters, convolution
– Spatial structure primitives: edge detectors & other filters, feature recognition
– Convolution: basics, padding, stride, object recognition, architectures
3. CNN foundations: LeNet, de novo feature learning, parameter sharing
– Key ideas: learn features, hierarchy, re-use parameters, back-prop filter learning
– CNN formalization: representations(Conv+ReLU+Pool)*N layers + Fully-connected
4. Modern CNN architectures: millions of parameters, dozens of layers
– Feature invariance is hard: apply perturbations, learn for each variation
– ImageNet progression of best performers
– AlexNet: First top performer CNN, 60M parameters (from 60k in LeNet-5), ReLU
– VGGNet: simpler but deeper (819 layers), 140M parameters, ensembles
– GoogleNet: new primitive=inception module, 5M params, no FC, efficiency
– ResNet: 152 layers, vanishing gradients fit residuals to enable learning
5. Countless applications: General architecture, enormous power
– Semantic segmentation, facial detection/recognition, self-driving, image
colorization, optimizing pictures/scenes, up-scaling, medicine, biology, genomics
31. Key idea:
learn hierarchy of features
directly from the data
(rather than hand-engineering them)
Low level features Mid level features High level features
Lee+ ICML 2009
Eyes,ears,nose
Edges,dark spots Facial structure
32. Key idea: re-use parameters
Convolution shares parameters
Example 3x3 convolution on a 5x5 image
33. Feature Extraction with Convolution
1) Apply a set of weights – a filter – to extract local features
2) Use multiple filters to extract different features
3) Spatially share parameters of each filter
34. LeNet-5
• Gradient Based Learning Applied To Document Recognition -
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner; 1998
• Helped establish how we use CNNs today
• Replaced manual feature extraction
[LeCun et al., 1998]
35. LeNet-5
⋮ ⋮
�
𝑦𝑦
32×32×1 28×28×6 14×14×6 10×10×16
5×5×16
120 84
5 × 5
s = 1
f = 2
s = 2
avg pool
5 × 5
s = 1
avg pool
f = 2
s = 2
. . .
. . .
Reminder:
Output size = (N+2P-F)/stride + 1
10
conv conv
FC FC
[LeCun et al., 1998]
This slide is taken from Andrew Ng
36. LeNet-5
• Only 60K parameters
• As we go deeper in the network: 𝑁𝑁𝐻𝐻 ↓, 𝑁𝑁𝑊𝑊↓, 𝑁𝑁𝐶𝐶 ↑
• General structure:
conv->pool->conv->pool->FC->FC->output
• Different filters look at different channels
• Sigmoid and Tanh nonlinearity
[LeCun et al., 1998]
40. Representation Learning in Deep CNNs
Mid level features
Low level features High level features
Edges,dark spots
Conv Layer 1
Lee+ ICML 2009
Eyes,ears,nose
Conv Layer 2
Facial structure
Conv Layer 3
41. CNNs for Classification
1. Convolution:Apply filters to generate feature maps.
2. Non-linearity:Often ReLU.
3. Pooling:Downsampling operation on each feature map.
Trainmodel with image data.
Learn weights of filters in convolutional layers.
tf.keras.layers.Conv2
D
tf.keras.activations.
*
tf.keras.layers.MaxPool2
D
43. Convolutional Layers: Local Connectivity
For a neuron in
hidden layer:
- Take inputs from patch
- Compute weighted
sum
- Apply bias
tf.keras.layers.
Conv2D
44. Convolutional Layers: Local Connectivity
For a neuron in hidden layer:
• Take inputs from patch
• Compute weighted sum
• Apply bias
4x4 filter:
matrix of
weights wij for neuron (p,q) in hidden layer
1) applying a window of weights
2) computing linear combinations
3) activating with non-linear function
tf.keras.layers.Conv2D
45. CNNs: Spatial Arrangement of Output
Volume
depth
width
height
Layer Dimensions:
ℎ w d
where h and w are spatial
dimensions d (depth) = number of
filters
Stride:
Filter step size
Receptive Field:
Locations in input image
that a node is path
connected to
tf.keras.layers.Conv2D( filters=d, kernel_size=(h,w), strides=s )
46. Introducing Non-Linearity
Rectified Linear Unit
(ReLU)
- Apply after every convolution operation
(i.e.,after convolutional layers)
- ReLU:pixel-by-pixel operation that replaces
all negative values by zero.
- Non-linear operation
tf.keras.layers.ReLU
Karn Intuitive CNNs
48. The REctified Linear Unit (RELU) is a common
non-linear detector stage after convolution
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
x= tf.nn.relu(x)
f(x) = max(0, x)
When will we backpropagate through this?
Once it “dies” what happens to it?
49. Pooling reduces dimensionality by giving up
spatial location
• max pooling reports the maximum output
within a defined neighborhood
• Padding can be SAME or VALID
x = tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
Output Input Pooling Batch H W Input channel
Neighborhood
[batch, height, width, channels]
51. 91
CNNs for Classification: Feature Learning
1. Learn features in input image through convolution
2. Introduce non-linearity through activation function (real-world data is
non-linear!)
3. Reduce dimensionality and preserve spatial invariance with pooling
52. CNNs for Classification: Class Probabilities
- CONV and POOL layers output high-level features of input
- Fully connected layer uses these features for classifying input image
- Express output as probability of image belonging to a particular class
53. Putting it all together
import tensorflow as tf
def generate_model():
model = tf.keras.Sequential([
# first convolutional layer
tf.keras.layers.Conv2D(32, filter_size=3, activation='relu’),
tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
# second convolutional layer
tf.keras.layers.Conv2D(64, filter_size=3, activation='relu’),
tf.keras.layers.MaxPool2D(pool_size=2, strides=2),
# fully connected classifier
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(1024, activation='relu’),
tf.keras.layers.Dense(10, activation=‘softmax’)
# 10 outputs
])
return model
54. Today: Convolutional Neural Networks (CNNs)
1. Scene understanding and object recognition for machines (and humans)
– Scene/object recognition challenge. Illusions reveal primitives, conflicting info
– Human neurons/circuits. Visual cortex layers==abstraction. General cognition
2. Classical machine vision foundations: features, scenes, filters, convolution
– Spatial structure primitives: edge detectors & other filters, feature recognition
– Convolution: basics, padding, stride, object recognition, architectures
3. CNN foundations: LeNet, de novo feature learning, parameter sharing
– Key ideas: learn features, hierarchy, re-use parameters, back-prop filter learning
– CNN formalization: representations(Conv+ReLU+Pool)*N layers + Fully-connected
4. Modern CNN architectures: millions of parameters, dozens of layers
– Feature invariance is hard: apply perturbations, learn for each variation
– ImageNet progression of best performers
– AlexNet: First top performer CNN, 60M parameters (from 60k in LeNet-5), ReLU
– VGGNet: simpler but deeper (819 layers), 140M parameters, ensembles
– GoogleNet: new primitive=inception module, 5M params, no FC, efficiency
– ResNet: 152 layers, vanishing gradients fit residuals to enable learning
5. Countless applications: General architecture, enormous power
– Semantic segmentation, facial detection/recognition, self-driving, image
colorization, optimizing pictures/scenes, up-scaling, medicine, biology, genomics
57. How can computers recognize objects?
Challenge:
• Objects can be anywhere in the scene, in any orientation, rotation, color hue, etc.
• How can we overcome this challenge?
Answer:
• Learn a ton of features (millions) from the bottom up
• Learn the convolutional filters, rather than pre-computing them
60. LeNet-5
• Gradient Based Learning Applied To Document Recognition -
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner; 1998
• Helped establish how we use CNNs today
• Replaced manual feature extraction
[LeCun et al., 1998]
61. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
62. AlexNet
• ImageNet Classification with Deep Convolutional
Neural Networks - Alex Krizhevsky, Ilya Sutskever,
Geoffrey E. Hinton; 2012
• Facilitated by GPUs, highly optimized convolution
implementation and large datasets (ImageNet)
• One of the largest CNNs to date
• Has 60 Million parameter compared to 60k
parameter of LeNet-5
[Krizhevsky et al., 2012]
63. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
• The annual “Olympics” of computer vision.
• Teams from across the world compete to see who has the
best computer vision model for tasks such as classification,
localization, detection, and more.
• 2012 marked the first year where a CNN was used to
achieve a top 5 test error rate of 15.3%.
• The next best entry achieved an error of 26.2%.
64. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
65. AlexNet
[Krizhevsky et al., 2012]
Architecture
CONV1
MAX POOL1
NORM1
CONV2
MAX POOL2
NORM2
CONV3
CONV4
CONV5
Max POOL3
FC6
FC7
FC8
• Input: 227x227x3 images (224x224 before
padding)
• First layer: 96 11x11 filters applied at stride 4
• Output volume size?
(N-F)/s+1 = (227-11)/4+1 = 55 ->
[55x55x96]
• Number of parameters in this layer?
(11*11*3)*96 = 35K
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
67. AlexNet
[Krizhevsky et al., 2012]
• Input: 227x227x3 images (224x224 before
padding)
• After CONV1: 55x55x96
• Second layer: 3x3 filters applied at stride 2
• Output volume size?
(N-F)/s+1 = (55-3)/2+1 = 27 -> [27x27x96]
• Number of parameters in this layer?
0!
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
Architecture
CONV1
MAX POOL1
NORM1
CONV2
MAX POOL2
NORM2
CONV3
CONV4
CONV5
Max POOL3
FC6
FC7
FC8
68. AlexNet
. . .
227×227 ×3 55×55 × 96 27×27 ×96 27×27 ×256
13×13
×256
13×13 ×384 13×13 ×384 13×13 ×256 6×6 ×256
11 × 11
s = 4
P = 0
3 × 3
s = 2
max pool
5 × 5
S = 1
P = 2
3 × 3
s = 2
max pool
3 × 3
S = 1
P = 1
3 × 3
s = 1
P = 1
3 × 3
S = 1
P = 1
3 × 3
s = 2
max pool
conv conv
conv conv conv
. . .
[Krizhevsky et al., 2012]
. . .
This slide is taken from Andrew Ng
69. AlexNet
. . .
4096 4096
Softmax
1000
⋮ ⋮
[Krizhevsky et al., 2012]
FC FC
This slide is taken from Andrew Ng
70. AlexNet
[Krizhevsky et al., 2012]
Details/Retrospectives:
• first use of ReLU
• used Norm layers (not common anymore)
• heavy data augmentation
• dropout 0.5
• batch size 128
• 7 CNN ensemble
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
71. AlexNet
[Krizhevsky et al., 2012]
• Trained on GTX 580 GPU with only 3 GB of memory.
• Network spread across 2 GPUs, half the neurons (feature
maps) on each GPU.
• CONV1, CONV2, CONV4, CONV5:
Connections only with feature maps on same GPU.
• CONV3, FC6, FC7, FC8:
Connections with all feature maps in preceding layer,
communication across GPUs.
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
72. AlexNet
AlexNet was the coming out party for CNNs in the computer
vision community. This was the first time a model performed
so well on a historically difficult ImageNet dataset. This
paper illustrated the benefits of CNNs and backed them up
with record breaking performance in the competition.
[Krizhevsky et al., 2012]
73. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
74. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
75. VGGNet
• Very Deep Convolutional Networks For Large Scale
Image Recognition - Karen Simonyan and Andrew
Zisserman; 2015
• The runner-up at the ILSVRC 2014 competition
• Significantly deeper than AlexNet
• 140 million parameters
[Simonyan and Zisserman, 2014]
76. VGGNet
• Smaller filters
Only 3x3 CONV filters, stride 1, pad 1
and 2x2 MAX POOL , stride 2
• Deeper network
AlexNet: 8 layers
VGGNet: 16 - 19 layers
• ZFNet: 11.7% top 5 error in ILSVRC’13
• VGGNet: 7.3% top 5 error in ILSVRC’14
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9. [Simonyan and Zisserman, 2014]
Input
3x3 conv, 64
3x3 conv, 64
Pool 1/2
3x3 conv, 128
3x3 conv, 128
Pool 1/2
3x3 conv, 256
3x3 conv, 256
Pool 1/2
3x3 conv, 512
3x3 conv, 512
3x3 conv, 512
Pool 1/2
3x3 conv, 512
3x3 conv, 512
3x3 conv, 512
Pool 1/2
FC 4096
FC 4096
FC 1000
Softmax
77. VGGNet
[Simonyan and Zisserman, 2014]
• Why use smaller filters? (3x3 conv)
Stack of three 3x3 conv (stride 1) layers has the same effective
receptive field as one 7x7 conv layer.
• What is the effective receptive field of three 3x3 conv (stride
1) layers?
7x7
But deeper, more non-linearities
And fewer parameters: 3 * (32C2) vs. 72C2 for C channels per layer
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
78. VGGNet
[Simonyan and Zisserman, 2014]
VGG16:
TOTAL memory: 24M * 4 bytes ~= 96MB / image
TOTAL params: 138M parameters
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
Input
3x3 conv, 64
3x3 conv, 64
Pool
3x3 conv, 128
3x3 conv, 128
Pool
3x3 conv, 256
3x3 conv, 256
3x3 conv, 256
Pool
3x3 conv, 512
3x3 conv, 512
3x3 conv, 512
Pool
3x3 conv, 512
3x3 conv, 512
3x3 conv, 512
Pool
FC 4096
FC 4096
FC 1000
Softmax
80. VGGNet
[Simonyan and Zisserman, 2014]
Details/Retrospectives :
• ILSVRC’14 2nd in classification, 1st in localization
• Similar training procedure as AlexNet
• No Local Response Normalisation (LRN)
• Use VGG16 or VGG19 (VGG19 only slightly better, more
memory)
• Use ensembles for best results
• FC7 features generalize well to other tasks
• Trained on 4 Nvidia Titan Black GPUs for two to three weeks.
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
81. VGGNet
VGG Net reinforced the notion that convolutional neural
networks have to have a deep network of layers in order for
this hierarchical representation of visual data to work.
Keep it deep.
Keep it simple.
[Simonyan and Zisserman, 2014]
82. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
83. GoogleNet
• Going Deeper with Convolutions - Christian Szegedy et
al.; 2015
• ILSVRC 2014 competition winner
• Also significantly deeper than AlexNet
• x12 less parameters than AlexNet
• Focused on computational efficiency
[Szegedy et al., 2014]
84. GoogleNet
• 22 layers
• Efficient “Inception” module - strayed from
the general approach of simply stacking conv
and pooling layers on top of each other in a
sequential structure
• No FC layers
• Only 5 million parameters!
• ILSVRC’14 classification winner (6.7% top 5
error)
[Szegedy et al., 2014]
85. GoogleNet
“Inception module”: design a good local network topology (network within
a network) and then stack these modules on top of each other
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9. [Szegedy et al., 2014]
Filter
concatenation
Previous layer
1x1
convolution
3x3
convolution
5x5
convolution
1x1
convolution
1x1
convolution
1x1
convolution
3x3 max
pooling
86. GoogleNet
Details/Retrospectives :
• Deeper networks, with computational efficiency
• 22 layers
• Efficient “Inception” module
• No FC layers
• 12x less params than AlexNet
• ILSVRC’14 classification winner (6.7% top 5 error)
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9. [Szegedy et al., 2014]
87. GoogleNet
Introduced the idea that CNN layers didn’t always have to be
stacked up sequentially. Coming up with the Inception
module, the authors showed that a creative structuring of
layers can lead to improved performance and
computationally efficiency.
[Szegedy et al., 2014]
88. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
89. ResNet
• Deep Residual Learning for Image Recognition -
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun;
2015
• Extremely deep network – 152 layers
• Deeper neural networks are more difficult to train.
• Deep networks suffer from vanishing and
exploding gradients.
• Present a residual learning framework to ease the
training of networks that are substantially deeper
than those used previously.
[He et al., 2015]
90. ResNet
• ILSVRC’15 classification winner (3.57% top 5
error, humans generally hover around a 5-
10% error rate)
Swept all classification and detection
competitions in ILSVRC’15 and COCO’15!
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9. [He et al., 2015]
91. ResNet
• What happens when we continue stacking deeper layers on a
convolutional neural network?
• 56-layer model performs worse on both training and test error
-> The deeper model performs worse (not caused by overfitting)!
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9. [He et al., 2015]
92. ResNet
• Hypothesis: The problem is an optimization problem. Very
deep networks are harder to optimize.
• Solution: Use network layers to fit residual mapping instead
of directly trying to fit a desired underlying mapping.
• We will use skip connections allowing us to take the activation
from one layer and feed it into another layer, much deeper
into the network.
• Use layers to fit residual F(x) = H(x) – x
instead of H(x) directly
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9. [He et al., 2015]
93. ResNet
Residual Block
Input x goes through conv-relu-conv series and gives us F(x).
That result is then added to the original input x. Let’s call that
H(x) = F(x) + x.
In traditional CNNs, H(x) would just be equal to F(x). So, instead
of just computing that transformation (straight from x to F(x)),
we’re computing the term that we have to add, F(x), to the
input, x.
[He et al., 2015]
95. ResNet
Full ResNet architecture:
• Stack residual blocks
• Every residual block has two 3x3 conv layers
• Periodically, double # of filters and
downsample spatially using stride 2 (in each
dimension)
• Additional conv layer at the beginning
• No FC layers at the end (only FC 1000 to
output classes)
[He et al., 2015]
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
96. ResNet
• Total depths of 34, 50, 101, or 152 layers for
ImageNet
• For deeper networks (ResNet-50+), use
“bottleneck” layer to improve efficiency
(similar to GoogLeNet)
[He et al., 2015]
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
97. ResNet
Experimental Results:
• Able to train very deep networks without degrading
• Deeper networks now achieve lower training errors as
expected
[He et al., 2015]
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
98. ResNet
The best CNN architecture that we currently have and is a
great innovation for the idea of residual learning.
Even better than human performance!
[He et al., 2015]
99. Accuracy comparison
The best CNN architecture that we currently have and is a
great innovation for the idea of residual learning.
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
100. Forward pass time and power
consumption
The best CNN architecture that we currently have and is a
great innovation for the idea of residual learning.
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
101. ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) winners
Slide taken from Fei-Fei & Justin Johnson & Serena Yeung. Lecture 9.
102. Today: Convolutional Neural Networks (CNNs)
1. Scene understanding and object recognition for machines (and humans)
– Scene/object recognition challenge. Illusions reveal primitives, conflicting info
– Human neurons/circuits. Visual cortex layers==abstraction. General cognition
2. Classical machine vision foundations: features, scenes, filters, convolution
– Spatial structure primitives: edge detectors & other filters, feature recognition
– Convolution: basics, padding, stride, object recognition, architectures
3. CNN foundations: LeNet, de novo feature learning, parameter sharing
– Key ideas: learn features, hierarchy, re-use parameters, back-prop filter learning
– CNN formalization: representations(Conv+ReLU+Pool)*N layers + Fully-connected
4. Modern CNN architectures: millions of parameters, dozens of layers
– Feature invariance is hard: apply perturbations, learn for each variation
– ImageNet progression of best performers
– AlexNet: First top performer CNN, 60M parameters (from 60k in LeNet-5), ReLU
– VGGNet: simpler but deeper (819 layers), 140M parameters, ensembles
– GoogleNet: new primitive=inception module, 5M params, no FC, efficiency
– ResNet: 152 layers, vanishing gradients fit residuals to enable learning
5. Countless applications: General architecture, enormous power
– Semantic segmentation, facial detection/recognition, self-driving, image
colorization, optimizing pictures/scenes, up-scaling, medicine, biology, genomics
108. Self-Driving Cars: Navigation from Visual Perception
Raw
Perception
I
(ex.camera)
Coarse
Maps
M
(ex.GPS)
Possible Control Commands
Amini+ ICRA 2019
109. End-to-End Framework for Autonomous Navigation
Entire model trained end-to-end
without any human labelling or annotations
Amini+ ICRA 2019
114. Breast Cancer Screening
6
.
Breast cancer case
missed by radiologist
but detected byAI
AI
MD
Readers
AI
MD
Readers
CNN-based system outperformed expert
radiologists at detecting breast
cancer from mammograms