Generative

Adversarial Networks:

When fake never

looked so real

Evan Ntavelis1,2

Dr. Iason Kastanis1

Philipp Schmid1

{ens, iks, psd}@csem.ch

1. Robotics & Machine Learning

CSEM SA

2. Computer Vision Lab

ETH Zürich

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CSEM at a glance – Close to industry

N A L

MZ

Zürich

Muttenz

Neuchâtel

Alpnach

Landquart

83.0Turnover

(mio CHF)

450Persons

175Industrial

clients

64European

projects

3

Technologies in focus at CSEM

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Unpaired Image-to-Image Translation using

Cycle-Consistent Adversarial Networks

Zhu et al. 2017

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AttnGAN: Fine-Grained Text to Image Generation

with Attentional Generative Adversarial Networks}

Xu et al 2018

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A Style-Based Generator Architecture

for Generative Adversarial Networks

Karras et al. 2018

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Semantic Image Synthesis with

Spatially-Adaptive Normalization

Park et al. 2019

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Source: datagrid.co.jp 2019

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Few-Shot Adversarial Learning of

Realistic Neural Talking Head Models

Zakharov et al. 2019

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Generative Adversarial Nets

• Introduced in 2014 by Ian

Goodfellow

• Rapidly Adopted

• Unprecedented Generational

Quality

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Generative Adversarial Nets

• An adversarial game between

two subnets:

• The Generator

• The Discriminator

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• In the era of Fake News do highly realistic images harbor dangers to

the society?

Deep Fakes

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Defense Mechanisms

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How can we use GANs in the industry?

The important question…

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• Gathering data is tedious and

costly

• Good quality labels require

even more effort

The Problem

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• Adversarial Domain Adaptation

• Train on a simulated data and

adapt for the use case

• Data Augmentation

• Learn how to generate new

samples to train with

• Generate images with desired

attributes

A Solution Using Adversarial Networks

Sources: CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Hoffman et al. 2017,

GAN-based Synthetic Medical Image Augmentation

for increased CNN Performance

in Liver Lesion Classification

Frid-Adar et al, 2018

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• GANs are not a panacea

• Nascent technology

• Difficult to train

• Require abundance of data

• Clever schemes may reduce the

effort

• Yet, very promising results

• Worth the effort!

But…

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Are you interested in being part of a highly stimulating environment

working on the latest Deep Learning Technologies?

We are hiring!

That’s all folks!