Adversarial Networks : When fake never looked so real Evan Ntavelis - - PowerPoint PPT Presentation
Generative Adversarial Networks : When fake never looked so real Evan Ntavelis 1,2 Dr. Iason Kastanis 1 Philipp Schmid 1 {ens, iks, psd}@csem.ch 1. Robotics & Machine Learning CSEM SA 2. Computer Vision Lab ETH Zrich CSEM at a glance
Evan Ntavelis1,2
Philipp Schmid1 {ens, iks, psd}@csem.ch
CSEM SA
ETH Zürich
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N A L M Z
Zürich Muttenz Neuchâtel Alpnach Landquart
Turnover (mio CHF)
Persons
Industrial clients
European projects
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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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adapt for the use case
samples to train with
attributes
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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effort
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