adversarial_workshop
adversarial_workshop copied to clipboard
ilblackdragon
Adversarial Workshop code and presentation
Adversarial Workshop
TensorFlow has taken the deep learning world by storm. This workshop will be led by one of TensorFlow’s main contributors, Illia Polosukhin. Illia’s hands-on workshop will cover:
-
Dropout - both for preventing overfitting and as mechanics to get "what model doesn't know" (confidence of prediction).
-
Augmenting data with adversarial examples - to prevent overfitting and speed up training
-
How to limit technical exploits of your models - e.g. how to use different methods to prevent your model going haywire, using different methods (confidence, adversarial examples, discriminator, separate classifiers or just simple whitelists).
References
- Gal and Ghahramani, 2015: https://arxiv.org/pdf/1512.05287v5
- Srivastava et al., 2015: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
- Goodfellow et al., 2015: https://arxiv.org/pdf/1412.6572v3
- Miyato et al., 2016: https://arxiv.org/pdf/1507.00677v9
ilblackdragon