My presentation for Kharkiv AI club about capsule networks. Introduction to capsule networks theory, basics. Links, references, explanations of capsules and routing
Introduction to Capsule Networks (CapsNets)Aurélien Géron
CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning.
You can view this presentation on YouTube at: https://youtu.be/pPN8d0E3900
NIPS 2017 Paper:
* Dynamic Routing Between Capsules,
* by Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
* https://arxiv.org/abs/1710.09829
The 2011 paper:
* Transforming Autoencoders
* by Geoffrey E. Hinton, Alex Krizhevsky and Sida D. Wang
* https://goo.gl/ARSWM6
CapsNet implementations:
* Keras w/ TensorFlow backend: https://github.com/XifengGuo/CapsNet-Keras
* TensorFlow: https://github.com/naturomics/CapsNet-Tensorflow
* PyTorch: https://github.com/gram-ai/capsule-networks
Book:
Hands-On Machine with Scikit-Learn and TensorFlow
O'Reilly, 2017
Amazon: https://goo.gl/IoWYKD
Github: https://github.com/ageron
Twitter: https://twitter.com/aureliengeron
Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networ...Edureka!
This Edureka "Deep Learning Tutorial" (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. Single Layer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
My presentation for Kharkiv AI club about capsule networks. Introduction to capsule networks theory, basics. Links, references, explanations of capsules and routing
Introduction to Capsule Networks (CapsNets)Aurélien Géron
CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning.
You can view this presentation on YouTube at: https://youtu.be/pPN8d0E3900
NIPS 2017 Paper:
* Dynamic Routing Between Capsules,
* by Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
* https://arxiv.org/abs/1710.09829
The 2011 paper:
* Transforming Autoencoders
* by Geoffrey E. Hinton, Alex Krizhevsky and Sida D. Wang
* https://goo.gl/ARSWM6
CapsNet implementations:
* Keras w/ TensorFlow backend: https://github.com/XifengGuo/CapsNet-Keras
* TensorFlow: https://github.com/naturomics/CapsNet-Tensorflow
* PyTorch: https://github.com/gram-ai/capsule-networks
Book:
Hands-On Machine with Scikit-Learn and TensorFlow
O'Reilly, 2017
Amazon: https://goo.gl/IoWYKD
Github: https://github.com/ageron
Twitter: https://twitter.com/aureliengeron
Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networ...Edureka!
This Edureka "Deep Learning Tutorial" (Blog: https://goo.gl/4zxMfU) will help you to understand about Deep Learning concepts in detail with multiple examples using TensorFlow. This Deep Learning tutorial is ideal for beginners who want to learn about deep learning, artificial intelligence, neural networks, tensorflow from scratch. Below are the topics covered in this tutorial:
1. What Is Deep Learning?
2. How Deep Learning Works?
3. Single Layer Perceptron (Early Deep Learning Models)
4. Single Layer Perceptron Examples
5. Limitations Of Single Layer Perceptron
6. Multi Layer Perceptron
7. Multi Layer Perceptron Examples
8. Demo on Deep Learning With TensorFlow
Overview on Optimization algorithms in Deep LearningKhang Pham
Overview on function optimization in general and in deep learning. The slides cover from basic algorithms like batch gradient descent, stochastic gradient descent to the state of art algorithm like Momentum, Adagrad, RMSprop, Adam.
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.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
[PR12] Inception and Xception - Jaejun YooJaeJun Yoo
Introduction to Inception and Xception
video: https://youtu.be/V0dLhyg5_Dw
Papers:
Going Deeper with Convolutions
Rethinking the Inception Architecture for Computer Vision
Inception-v4, Inception-RestNet and the Impact of Residual Connections on Learning
Xception: Deep Learning with Depthwise Separable Convolutions
발표자: 배재성(KAIST 석사과정)
발표일: 2018.10.
최근 딥러닝을 이용한 방법은 다양한 음성 인식 과제에서 괄목할 만한 성과를 내고 있습니다. 특히 Convolutional Neural Network (CNN)을 이용한 방식은 지역적인 특징 (local feature)들을 효과적으로 잡아낼 수 있기 때문에 비교적 짧은 시간 의존도를 가지는 음성 키워드 인식이나 음소 단위 인식과 같은 과제들에서 활발히 사용되고 있습니다. 그러나 CNN은 낮은 레벨의 특징들 간의 공간적 관계성을 고려하지 않는다는 한계점이 있습니다. 이를 극복하기 위해 캡슐 네트워크 구조를 도입하여 음성 스펙트로그램에서 추출된 특징들의 공간적 관계성을 고려하고자 하였습니다. 구글 음성 단어 데이터셋에서 CNN과 그 성능을 비교해 보았으며, 깨끗한 환경과 잡음 환경 모두에서 주목할만한 성능 향상을 이끌어 냈습니다.
Overview on Optimization algorithms in Deep LearningKhang Pham
Overview on function optimization in general and in deep learning. The slides cover from basic algorithms like batch gradient descent, stochastic gradient descent to the state of art algorithm like Momentum, Adagrad, RMSprop, Adam.
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.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
[PR12] Inception and Xception - Jaejun YooJaeJun Yoo
Introduction to Inception and Xception
video: https://youtu.be/V0dLhyg5_Dw
Papers:
Going Deeper with Convolutions
Rethinking the Inception Architecture for Computer Vision
Inception-v4, Inception-RestNet and the Impact of Residual Connections on Learning
Xception: Deep Learning with Depthwise Separable Convolutions
발표자: 배재성(KAIST 석사과정)
발표일: 2018.10.
최근 딥러닝을 이용한 방법은 다양한 음성 인식 과제에서 괄목할 만한 성과를 내고 있습니다. 특히 Convolutional Neural Network (CNN)을 이용한 방식은 지역적인 특징 (local feature)들을 효과적으로 잡아낼 수 있기 때문에 비교적 짧은 시간 의존도를 가지는 음성 키워드 인식이나 음소 단위 인식과 같은 과제들에서 활발히 사용되고 있습니다. 그러나 CNN은 낮은 레벨의 특징들 간의 공간적 관계성을 고려하지 않는다는 한계점이 있습니다. 이를 극복하기 위해 캡슐 네트워크 구조를 도입하여 음성 스펙트로그램에서 추출된 특징들의 공간적 관계성을 고려하고자 하였습니다. 구글 음성 단어 데이터셋에서 CNN과 그 성능을 비교해 보았으며, 깨끗한 환경과 잡음 환경 모두에서 주목할만한 성능 향상을 이끌어 냈습니다.
An illustrative introduction on CNN.
Maybe one of the most visually understandable but precise slide on CNN in your life.
I made this slide as an intern in DATANOMIQ Gmbh
URL: https://www.datanomiq.de/
*This slide is not finished yet. If you like it, please give me some feedback to motivate me.
By: Jyoti Prakash Maheswari, Graduate Student in Data Science program at University of San Francisco
This presentation is prepared in partial fulfillment of the requirements of Business Communication(MSN610) course at the University of San Francisco. In this talk i discuss the major issues faced by CNN and how Capsule networks help address them.
Distributed Systems Theory for Mere Mortals - Topconf Dusseldorf October 2017Ensar Basri Kahveci
A talk about some of the core theoretical topics of distributed systems. It discusses system models, failure modes, time, the consensus problem, consistency models and high-level design principles: such as CAP and PACELC.
Deep Neural Networks that talk (Back)… with styleRoelof Pieters
Talk at Nuclai 2016 in Vienna
Can neural networks sing, dance, remix and rhyme? And most importantly, can they talk back? This talk will introduce Deep Neural Nets with textual and auditory understanding and some of the recent breakthroughs made in these fields. It will then show some of the exciting possibilities these technologies hold for "creative" use and explorations of human-machine interaction, where the main theorem is "augmentation, not automation".
http://events.nucl.ai/track/cognitive/#deep-neural-networks-that-talk-back-with-style
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-chiu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit.
The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used to the design of the neural network algorithm to the implementation of the algorithm on the target platform. This talk delves into how to evaluate the runtime performance of a CNN from a software architecture standpoint. It then explains in detail how to build a neural network from the ground up based on the requirements of the target hardware platform.
Chiu shares his ideas on how to improve performance without sacrificing accuracy, by applying recent research on training very deep networks. He also shows examples of how network optimization can be achieved at the algorithm design level by making a more efficient use of weights before the model is compressed via more traditional methods for deployment in a real-time application.
Distance-based bias in model-directed optimization of additively decomposable...Martin Pelikan
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Accelerating Science with Generative Adversarial NetworksMichela Paganini
Presentation at NERSC Data Day 2017 at Lawrence Berkeley National Laboratory on the potential of Generative Adversarial Networks to speed up scientific simulation and empower scientists and researchers.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
1. Dynamic Routing Between Capsules
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv
LAB SEMINAR
1
2017.11.13
SNU DATAMINING CENTER
MINKI CHUNG
2. TABLE OF CONTENTS
▸ Intuition
▸ Problems of ConvNet
▸ How brain works, Inverse graphics
▸ Capsule Theory
▸ CapsNet
▸ Capsule
▸ CapsNet architecture
▸ Experiment
▸ Classification on MNIST
▸ Reconstruction on MNIST
▸ Dimension perturbation on MNIST
▸ Discussion
2
4. PROBLEMS OF CONVNET 4
▸ ConvNet Architecture
PROBLEMS IS ‘POOLING’
https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc
Obtain translational, rotational invariance
5. PROBLEMS OF CONVNET 5
▸
@REDDIT, MACHINE LEARNING
https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/clyj4jv/
6. PROBLEMS OF CONVNET 6
▸
WHAT IS THIS PICTURE?
https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952
7. PROBLEMS OF CONVNET 7
▸
HOW ABOUT THIS?
https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952
8. PROBLEMS OF CONVNET 8
▸
NEED EQUIVARIANCE, NOT INVARIANCE
https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952
9. HOW BRAIN WORKS, INVERSE GRAPHICS 9
▸ Constructing a visual image from some internal hierarchical representation of
geometric data
▸ Internal representation is stored in computer’s memory as arrays of geometrical
objects and matrices that represent relative positions and orientation of these
objects
▸ Special software takes that representation and converts it into an image on the screen.
This is called rendering
▸ Brains, in fact, do the opposite of rendering. Hinton calls it inverse graphics: Visual
information received by eyes, they deconstruct a hierarchical representation of the
world around us and try to match it with already learned patterns and relationships
stored in the brain
▸ Key idea is that representation of objects in the brain does not depend on view angle
COMPUTER GRAPHICS
https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
10. CAPSULE THEORY 10
▸ In 3D graphics, relationships between 3D objects can be represented by a so-
called pose, which is in essence translation plus rotation
▸ Capsule approach: It incorporates relative relationships between objects (Internal
representation) and it is represented numerically as a 4D pose matrix
▸ by ‘Dynamic Routing’ (more details later)
▸ allows capsules to communicate with each other and create representations
similar to scene graphs in computer graphics
https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
YOU CAN EASILY RECOGNIZE THAT THIS IS THE STATUE OF LIBERTY,
EVEN THOUGH ALL THE IMAGES SHOW IT FROM DIFFERENT ANGLES
11. CAPSULE THEORY 11
▸ Benifits:
▸ Better understanding 3D Space
▸ Achieve state-of-the art performance by only using a fraction of the data that a CNN
would use
▸ In order to learn to tell digits apart, the human brain needs only a couple of dozens of
examples, hundreds at most, while CNN need tens of thousands of examples
https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
13. CAPSULE 13
▸ Comparison with traditional neuron
https://www.zhihu.com/question/67287444/answer/251460831
V
VEC LENGTH WORKS LIKE PROBABILITY
ACTIVATION OF NEXT CAPSULE
DYNAMIC ROUTING
14. CAPSNET ARCHITECTURE 14
ARCHITECTURE
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv. Dynamic Routing Between Capsules
CONV CAPS.CONV CAPS.FC
DYNAMIC ROUTING
8X
32
X
MNIST
LOCAL FEATURE DETECTION
6*6*32=1152 CAPSULES,
EACH HAS 8 PROPERTIES
10 CAPSULES (CLASS),
EACH HAS 16 PROPERTIES
DEEPER MEANS MORE COMPLEX, DIMENSION SHOULD INCREASE
15. CAPSNET ARCHITECTURE 15
▸ naturomics github
CAPSNET-TENSORFLOW
CAPS.CONVCONV
CONV
X 32
MNIST
X 8
https://github.com/naturomics/CapsNet-Tensorflow
X 32
X 8
CAPS.FC
CAPS.CONV
CAPS.FC
DYNAMIC ROUTING
16. CAPSNET ARCHITECTURE 16
▸ Place-coded Capsule
▸ Concatenate (=8 different regular conv layers)
▸ Consider each feature map as capsule (6*6*32=1152 capsules with 8
properties)
CAPS.CONV, PRIMARYCAPS
CAPS.CONV
X 32
MNIST
X 8
https://github.com/naturomics/CapsNet-Tensorflow
DIRECTION
17. CAPSNET ARCHITECTURE 17
▸ Place-coded Capsule
▸ Concatenate (=8 different regular conv layers)
▸ Consider each feature map as capsule (6*6*32=1152 capsules with 8
properties)
▸ Use squashing function in the end
CAPS.CONV, PRIMARYCAPS
CAPS.CONV
X 32
MNIST
X 8
https://github.com/naturomics/CapsNet-Tensorflow
19. CAPSNET ARCHITECTURE 19
▸ Dynamic Routing
▸ Top-down feedback
▸ Routing by agreement
▸ Works like attention
CAPS.FC, DIGITCAPS
https://github.com/naturomics/CapsNet-Tensorflow
IF MULTIPLE PREDICTIONS
AGREE, HIGHER LEVEL CAPSULE
BECOMES ACTIVE
VEC LENGTH WORKS LIKE PROBABILITY
ACTIVATION OF NEXT CAPSULE
COUPLING COEFFICIENTS
TOPDOWN FEEDBACK: IF RELATION EXISTS COUPLING COEFFICIENTS INCREASE
AGREEMENT
20. CAPSNET ARCHITECTURE 20
▸ Dynamic Routing
CAPS.FC, DIGITCAPS
https://github.com/naturomics/CapsNet-Tensorflow
X 32
MNIST
X 8
CAPS.FC
DYNAMIC ROUTING
3 ITERATIONS WILL DO
22. EXPERIMENT 22
▸ Introduce first three
▸ Classification on MNIST (99.75%, conv 99.61%)
▸ Reconstruction on MNIST
▸ Dimension Perturbation on MNIST
▸ Robustness to Affine Transformation on MNIST (79%, conv 66%)
▸ Classification on MultiMNIST (5% error)
▸ Classification on CIFAR 10 (10.6% error - ZFNet)
▸ Classification on SVHN (4.3% error)
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv. Dynamic Routing Between Capsules
23. EXPERIMENT 23
▸ 99.75% (baseline 99.61%)
1. CLASSIFICATION ON MNIST
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv. Dynamic Routing Between Capsules
24. EXPERIMENT 24
▸
2. RECONSTRUCTION ON MNIST
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv. Dynamic Routing Between Capsules
25. EXPERIMENT 25
▸
3. DIMENSION PERTURBATION ON MNIST
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv. Dynamic Routing Between Capsules
28. _ 28
▸ Still use regular conv layer at first for local feature extraction
▸ Capsule cannot extract local feature?
STILL USE CONV LAYER
HOW TO RESTRICT TO GET CERTAIN FEATURE?
▸ Disentangling features
▸ How to obtain ‘certain features’?
30. REFERENCE
▸ Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, 10, 2017, Arxiv. Dynamic Routing Between Capsules (https://
arxiv.org/abs/1710.09829)
▸ Geoffrey Hinton et al., Matrix Capsules With EM Routing, Under review as a conference paper at ICLR 2018 (https://
openreview.net/pdf?id=HJWLfGWRb)
▸ https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
▸ https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc
▸ https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952
▸ https://github.com/naturomics/CapsNet-Tensorflow
▸ https://www.zhihu.com/question/67287444/answer/251460831
▸ https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/clyj4jv/
▸ Geoffrey Hinton: "Does the Brain do Inverse Graphics?” (https://www.youtube.com/watch?
v=TFIMqt0yT2I&feature=youtu.be)
▸ Geoffrey Hinton talk "What is wrong with convolutional neural nets ?” (https://www.youtube.com/watch?
v=rTawFwUvnLE&t=1214s)
▸ https://www.youtube.com/watch?v=u50nqWMQe1k
30