The document provides an introduction to generative adversarial networks (GANs) in 3 paragraphs. It explains that a GAN is composed of two neural networks - a generator and discriminator. The generator takes random inputs and outputs generated data, like images. The discriminator takes real and generated data and tries to classify them as real or fake. The two networks are trained in an adversarial manner, with the generator trying to fool the discriminator and the discriminator trying to improve at detecting fakes.
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
A basic introduction to Generative Adversarial Networks, what they are, how they work, and why study them. This presentation shows what is their contribution to Machine Learning field and for which reason they have been considered one of the major breakthroughts in Machine Learning field.
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
A basic introduction to Generative Adversarial Networks, what they are, how they work, and why study them. This presentation shows what is their contribution to Machine Learning field and for which reason they have been considered one of the major breakthroughts in Machine Learning field.
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
This is how Generative Adversarial Networks (GANs) work and benefit the tech and dev industry. Although GANs still have room for improvement, GANs are important generative models that learn how to create realistic samples.
GANS
Ian Goodfellow, OpenAI Research Scientist
발표자: 박태성 (UC Berkeley 박사과정)
발표일: 2017.6.
Taesung Park is a Ph.D. student at UC Berkeley in AI and computer vision, advised by Prof. Alexei Efros.
His research interest lies between computer vision and computational photography, such as generating realistic images or enhancing photo qualities. He received B.S. in mathematics and M.S. in computer science from Stanford University.
개요:
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
However, for many tasks, paired training data will not be available.
We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.
Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Introduction to Generative Adversarial Networks (GAN) with Apache MXNetAmazon Web Services
GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
Slides by Víctor Garcia about the paper:
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial text to image synthesis." ICML 2016.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now 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 text captioning.
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
This is how Generative Adversarial Networks (GANs) work and benefit the tech and dev industry. Although GANs still have room for improvement, GANs are important generative models that learn how to create realistic samples.
GANS
Ian Goodfellow, OpenAI Research Scientist
발표자: 박태성 (UC Berkeley 박사과정)
발표일: 2017.6.
Taesung Park is a Ph.D. student at UC Berkeley in AI and computer vision, advised by Prof. Alexei Efros.
His research interest lies between computer vision and computational photography, such as generating realistic images or enhancing photo qualities. He received B.S. in mathematics and M.S. in computer science from Stanford University.
개요:
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
However, for many tasks, paired training data will not be available.
We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.
Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Introduction to Generative Adversarial Networks (GAN) with Apache MXNetAmazon Web Services
GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
Slides by Víctor Garcia about the paper:
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial text to image synthesis." ICML 2016.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now 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 text captioning.
Generation of Deepfake images using GAN and Least squares GAN.pptDivyaGugulothu
Our project is to generate deep fake images using deep learning techniques i.e Generative
Adversarial Networks.
We generated Deep Fake images using Traditional GAN and least squares GAN.
GANs, short for Generative Adversarial Networks, are a type of generative model based on deep learning. They were first introduced in the 2014 paper “Generative Adversarial Networks” by Ian Goodfellow and his team. GANs are a type of neural network used for unsupervised learning, meaning they can create new data without being explicitly told what to generate. To understand GANs, having some knowledge of Convolutional Neural Networks (CNNs) is helpful. CNNs are used to classify images based on their labels. In contrast, GANs can be divided into two parts: the Generator and the Discriminator. The Discriminator is similar to a CNN, as it is trained on real data and learns to recognize what real data looks like. However, the Discriminator only has two output values – 1 or 0 – depending on whether the data is real or fake. The Generator, on the other hand, is an inverse CNN. It takes a random noise vector as input and generates new data based on that input. The Generator’s goal is to create realistic data that can fool the Discriminator into thinking it’s real. The Generator keeps improving its output until the Discriminator can no longer distinguish between real and generated data.
Convolutional Neural Networks (CNNs) are the preferred models for both the generator and discriminator in Generative Adversarial Networks (GANs), typically used with image data. This is because the original concept of GANs was introduced in computer vision, where CNNs had already shown remarkable progress in tasks such as face recognition and object detection. By modeling image data, the generator’s input space, also known as the latent space, provides a compressed representation of the image or photograph set used to train the GAN model. This makes it easy for developers or users of the model to assess the quality of the output, as it is in a visually assessable form. This attribute, among others, has likely contributed to the focus on CNNs for computer vision applications and the incredible advancements made by GANs compared to other generative models, whether they are based on deep learning or not.
How to create a neural network that detects people wearing masks. Ultimate description, the A-to-Z workflow for creating a neural network that recognizes images.
A short intro to the paper: https://blog.fulcrum.rocks/neural-network-image-recognition
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now 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 text captioning.
Our project is to generate deep fake images using deep learning techniques i.e Generative
Adversarial Networks.
We need to create Deep Fake images using Traditional GAN and least squares GAN.
[AI07] Revolutionizing Image Processing with Cognitive Toolkitde:code 2017
Deep Learning has revolutionized the field of image processing. I'll show real-world examples using CNTK, from anomaly classification using CNNs to generation using Generative Adversarial Networks.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Microsoft Azure/Machine Learning (機械学習)
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
https://telecombcn-dl.github.io/2018-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.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
ESC Beyond Borders _From EU to You_ InfoPack general.pdf
Introduction To Generative Adversarial Networks GANs
1. Introduction To Generative
Adversarial Networks
GANs
Hichem FELOUAT
SAAD DAHLAB BLIDA UNIVERSITY - Algeria - 2020
FACULTY OF SCIENCE - Computer Science Department
LRDSI Laboratory
hichemfel@gmail.com
2. Introduction
2Hichem Felouat - 2020 - hichemfel@gmail,com
• Generative adversarial networks (GANs) were proposed in a 2014 paper [1].
• A GAN is composed of two neural networks (Generator & Discriminator).
• Generator: Takes a random distribution as input (typically Gaussian) and
outputs some data - typically, an image. You can think of the random inputs
as the latent representations (i.e., codings) of the image to be generated.
• Discriminator: Takes either a fake image from the generator or a real image
from the training set as input, and must guess whether the input image is
fake or real.
[1] Ian Goodfellow et al., “Generative Adversarial Nets,” Proceedings of the 27th International Conference on Neural Information
Processing Systems 2 (2014): 2672–2680.
4. Hichem Felouat - 2020 - hichemfel@gmail,com 4
Applications of GAN
1) Generate Examples for
Image Datasets
2) Generate Photographs
of Human Faces
3) Generate Realistic
Photographs
4) Generate Cartoon
Characters
5) Image-to-Image
Translation
6) Text-to-Image
Translation
7) Semantic-Image-to-
Photo Translation
8) Face Frontal View
Generation
9) Generate New
Human Poses
10) Photos to Emojis
11) Photograph Editing
12) Face Aging
13) Photo Blending
14) Super Resolution
15) Photo Inpainting
16) Clothing
Translation
17) Video Prediction
18) 3D Object
Generation
Generative adversarial networks: a survey on applications and challenges
https://link.springer.com/article/10.1007/s13735-020-00196-w
gans-awesome-applications
https://github.com/nashory/gans-awesome-applications
https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/
5. Hichem Felouat - 2020 - hichemfel@gmail,com 5
GAN Training
• The generator and the discriminator have opposite
goals: the discriminator tries to tell fake images from real
images, while the generator tries to produce images that
look real enough to trick the discriminator.
• Because the GAN is composed of two networks with
different objectives, it can not be trained like a regular
neural network. Each training iteration is divided into two
phases:
7. Hichem Felouat - 2020 - hichemfel@gmail,com 7
GAN Training
First phase:
• We train the discriminator. A batch of real images is sampled from the
training set and is completed with an equal number of fake images produced
by the generator (The labels are: 0 = fake images and 1 = real images).
• The discriminator is trained on this labeled batch for one step, using the
binary cross-entropy loss.
• Backpropagation only optimizes the weights of the discriminator during this
phase.
8. Hichem Felouat - 2020 - hichemfel@gmail,com 8
GAN Training
Second phase:
• We train the generator. We first use it to produce another batch of fake
images, and once again the discriminator is used to tell whether the images
are fake or real.
• This time we do not add real images in the batch (The generator never
actually sees any real images).
• The weights of the discriminator are frozen during this step, so
backpropagation only affects the weights of the generator.
9. Hichem Felouat - 2020 - hichemfel@gmail,com 9
Common Problems
• Vanishing Gradients: when the discriminator doesn't provide enough
information for the generator to make progress (The original GAN paper
proposed a modification to minimax loss to deal with vanishing gradients)[2].
• Mode Collapse: this is when the generator starts producing the same output
(or a small set of outputs) over and over again. How can this happen?
Suppose that the generator gets better at producing convincing (class1) than
any other class. It will fool the discriminator a bit more with (class1), and this
will encourage it to produce even more images of (class1). Gradually, it will
forget how to produce anything else.
• GANs are very sensitive to the hyperparameters: you may have to spend
a lot of effort fine-tuning them.
[2] https://developers.google.com/machine-learning/gan/loss
10. Hichem Felouat - 2020 - hichemfel@gmail,com 10
Deep Convolutional GANs
Deep Convolutional GANs (DCGANs) - 2015
Alec Radford et al., “Unsupervised Representation Learning with Deep
Convolutional Generative Adversarial Networks,” arXiv preprint
arXiv:1511.06434 (2015).
11. Hichem Felouat - 2020 - hichemfel@gmail,com 11
Deep Convolutional GANs
Here are the main guidelines they proposed for building stable
convolutional GANs:
1) Replace any pooling layers with strided convolutions (in the discriminator) and
transposed convolutions (in the generator).
2) Use Batch Normalization in both the generator and the discriminator, except in the
generator’s output layer and the discriminator’s input layer.
3) Remove fully connected hidden layers for deeper architectures.
4) Use ReLU activation in the generator for all layers except the output layer, which should
use tanh.
5) Use leaky ReLU activation in the discriminator for all layers.
12. Hichem Felouat - 2020 - hichemfel@gmail,com 12
Example: Preparing The Dataset cifar10
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
# Using Keras to load the dataset
(X_train, y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()
print("X_train shape = ",X_train.shape," X_test shape = ",X_test.shape)
fig = plt.figure()
for i in range(9):
plt.subplot(3,3,i+1)
plt.tight_layout()
plt.imshow(X_train[i], cmap='gray', interpolation='none')
plt.xticks([])
plt.yticks([])
# Scale the pixel intensities down to the [0,1] range by dividing them by 255.0
X_train = X_train.astype("float32") / 255.0
# Creating a Dataset to iterate through the images
batch_size = 128
dataset = tf.data.Dataset.from_tensor_slices(X_train).shuffle(1000)
dataset = dataset.batch(batch_size, drop_remainder=True).prefetch(1)
13. Hichem Felouat - 2020 - hichemfel@gmail,com 13
Example: The Generator
# codings_size : the dimension of the input vector for the generator
codings_size = 100
def build_generator(codings_size=100):
generator = tf.keras.Sequential()
# latent variable as input
generator.add(keras.layers.Dense(1024, activation="relu", input_shape=(codings_size,)))
generator.add(keras.layers.BatchNormalization())
generator.add(keras.layers.Dense(1024, activation="relu"))
generator.add(keras.layers.BatchNormalization())
generator.add(keras.layers.Dense(128*8*8, activation="relu"))
generator.add(keras.layers.Reshape((8, 8, 128)))
assert generator.output_shape == (None, 8, 8, 128) # Note: None is the batch size
generator.add(keras.layers.Conv2DTranspose(filters=128, kernel_size=2, strides=2, activation="relu", padding="same"))
assert generator.output_shape == (None, 16, 16, 128)
generator.add(keras.layers.BatchNormalization())
generator.add(keras.layers.Conv2DTranspose(filters=3, kernel_size=2, strides=2, activation="tanh", padding="same"))
assert generator.output_shape == (None, 32, 32, 3)
generator.add(keras.layers.BatchNormalization())
return generator
14. Hichem Felouat - 2020 - hichemfel@gmail,com 14
Example: The Generator plot generated images
generator = build_generator()
nbr_imgs = 3
def plot_generated_images(nbr_imgs, titleadd=""):
noise = tf.random.normal([nbr_imgs, 100])
imgs = generator.predict(noise)
fig = plt.figure(figsize=(40,10))
for i, img in enumerate(imgs):
ax = fig.add_subplot(1,nbr_imgs,i+1)
ax.imshow((img * 255).astype(np.uint8))
fig.suptitle("Generated images"+titleadd,fontsize=25)
plt.show()
plot_generated_images(nbr_imgs)
In the beginning, the generator
generates random pictures.
22. Hichem Felouat - 2020 - hichemfel@gmail,com 22
GANs in NLP
In this paper, the author explores the uses of GAN in this NLP task and
proposed a GAN architecture that does the same.
https://arxiv.org/abs/1905.01976
23. Hichem Felouat - 2020 - hichemfel@gmail,com 23
thank you for your
attention
Hichem Felouat 888