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Distribution Augmentation for Generative Modeling

This video explains a recent paper from OpenAI exploring how to improve generative models with data augmentation. DistAug conditions models on the transformation in a multi-task learning way. This results in improved performance, particularly with more parameters, more augmentations, and less dropout! Thanks for watching! Please Subscribe! Paper Links: DistAug: https://proceedings.icml.cc/static/pa... ImageGPT: https://openai.com/blog/image-gpt/ A Survey on Image Data Augmentation: https://link.springer.com/article/10.... Training GANs with Limited Data: https://arxiv.org/abs/2006.06676 Chapters 0:00 Beginning 1:36 Data Augmentation in Computer Vision 4:20 Challenge of Data Aug in Generative Modeling 6:24 DistAug, condition on augmentation embedding 8:20 Start of Sequence token embedding 11:20 Data-Dependent Regularization and Multi-Task Learning 12:32 Examples of Generated Images and Nearest Neighbors in the original dataset 13:34 Benefits of Scale - ImageGPT-2? 15:23 Regularizing Deep Learning, Data Augmentation vs. Dropout / Weight Decay / L2 Regularization 16:53 Why does Rotation augmentation work better than Jigsaw?