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Kyryl Truskovskyi "Data Augmentation with GANs"

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Kyryl Truskovskyi "Data Augmentation with GANs"

  1. 1. Kyryl Truskovskyi Data Augmentation with GANs
  2. 2. The problem we are going to solve
  3. 3. Source: A systematic study of the class imbalance problem in convolutional neural networks https://arxiv.org/abs/1710.05381 The Class Imbalance Problem (a) ρ = 10, µ = 0.5 (b) ρ = 2, µ = 0.9 (c) ρ = 10
  4. 4. The Class Imbalance Problem Source: A systematic study of the class imbalance problem in convolutional neural networks https://arxiv.org/abs/1710.05381 (a) MNIST (b) CIFAR-10
  5. 5. The Class Imbalance Problem Source: A systematic study of the class imbalance problem in convolutional neural networks https://arxiv.org/abs/1710.05381 2 minority classes 5 minority classes 8 minority classes (a) (d) (b) (e) (c) (f)
  6. 6. What do the GANs have to do with it?
  7. 7. The GANs Source: Large Scale GAN Training for High Fidelity Natural Image Synthesis https://arxiv.org/abs/1809.11096
  8. 8. The GANs Source: Large Scale GAN Training for High Fidelity Natural Image Synthesis https://arxiv.org/abs/1809.11096
  9. 9. The GANs Source: Generative Adversarial Networks https://arxiv.org/abs/1406.2661
  10. 10. Is it possible to train using GANs generated synthetic datasets only?
  11. 11. Is it possible to train using GANs generated synthetic datasets only? NO
  12. 12. A Classification-Based Study of Covariate Shift in GAN Distributions https://arxiv.org/abs/1711.00970
  13. 13. But…
  14. 14. Successful GANs application in the medicine Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks https://arxiv.org/abs/1807.10225
  15. 15. Successful GANs application for gaze estimation Learning from Simulated and Unsupervised Images through Adversarial Training https://arxiv.org/abs/1612.07828
  16. 16. Successful GANs application for augmentation Data Augmentation Generative Adversarial Networks https://arxiv.org/abs/1612.07828
  17. 17. BAGAN: Data Augmentation with Balancing GAN https://arxiv.org/abs/1803.09655 What will we do today? AccuracyAccuracy Percentage of minority-class images dropped from the train set [%]
  18. 18. Let’5 c0d3!
  19. 19. What’s next?
  20. 20. Q&A
  21. 21. neuromation-back.paperform.co neuromation-front.paperform.co Front EndBack End Oh, wow! We’re hiring!

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