An Introduction to Generative Adversarial Networks (April 2020)

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© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
An Introduction toGenerative
Adversarial Networks
Julien Simon
Global Evangelist, AI & Machine Learning
Amazon Web Services
@julsimon
M L T 4

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
What are Generative Adversarial Networks?
• Most Machine Learning problems try to make sense of a real-life dataset
• Tabular data: regression, classification, clustering
• Text: sentiment analysis, entity extraction
• Image: object detection, image segmentation
• Generative Adversarial Networks are fundamentally different
• Goodfellow, 2014 https://arxiv.org/abs/1406.2661
• A GAN uses a real-life dataset to generate new lookalike samples
• Not based on statistical analysis, e.g. filling in missing values during feature engineering

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Adversarial Networks
• Two deep learning networks compete against one another
• The Generator network creates new samples from random data,
not from the data set
• Start from a random vector (say, 100 bytes) and generate a tensor with the same
shape as dataset samples (say, 3x256x256)
• The Discriminator learns how to classify samples
• Real ones, coming from the data set
• Fake ones, generated by the Generator
• Typical Deep Learning process: mini-batch training, backpropagation, etc.

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« How do you create something from nothing? »
« How can fake samples look remotely
convincing? »
« How does the Generator learn? »

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Let’s take about convolution for a minute
• Convolution extracts features
from complex data samples
• It reduces the total amount of
information
• Samples can shrink, keeping
only the « good stuff »
• Padding and stride
• So we know how to go from (3,
256, 256) to [0,1]
• From images to probabilities
• Can we do the opposite?

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Transposed convolution
• Transposed convolution is the reverse operation
• It increases the total amount of information
• Samples grow
• So we know how to go from (100,) to (3, 256, 256)

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The Deep Convolutional Generator (DCGAN)
https://arxiv.org/abs/1511.06434

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Deep Convolutional Discriminator (DCGAN)
https://arxiv.org/abs/1511.06434

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Training DCGAN
https://medium.com/@julsimon/generative-adversarial-networks-on-apache-mxnet-part-1-b6d39e6b5df1
Generator
Building images from random vectors
Detector
Learning to detect real and fake samples
Gradient updates
Fake images
Real images

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Demo:
training DCGAN with MXNetGluon
https://gitlab.com/juliensimon/dlnotebooks/-/blob/master/mxnet/07%20-%20DCGAN%20Gluon.ipynb

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
GAN: Welcome to the (un)real world, Neo
Generating new ”celebrity” faces
https://github.com/tkarras/progressive_growing_of_gans
April 2018
From semantic map to 2048x1024 picture
https://tcwang0509.github.io/pix2pixHD/
November 2017
TF
PyTorch

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
More face generation with GANs
Controlled Image Generation with TL-GAN
https://github.com/SummitKwan/transparent_latent_gan
October 2018
Applying the style of a face to another face
https://github.com/NVlabs/stylegan
https://www.youtube.com/watch?v=kSLJriaOumA
March 2019
TF

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
GAN: Everybody dance now
https://arxiv.org/abs/1808.07371
https://www.youtube.com/watch?v=PCBTZh41Ris
August 2018

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Autodesk - Generative Design
https://www.youtube.com/watch?v=A31A8KDC9S4

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS DeepComposer
https://aws.amazon.com/deepcomposer
https://github.com/aws-samples/aws-deepcomposer-samples

© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Resources
https://aws.training/machinelearning
https://deeplearning.ai
https://fast.ai
http://www.deeplearningbook.org/
https://d2l.ai/
https://gitlab.com/juliensimon/dlnotebooks

Thank you!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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