This problem is important to robustly learning from data that are corrupted by,e.g., random noise and adversarial examples. The standard empirical risk minimization (ERM) for such data, however, may easily overfit noise and thus suffers from sub-optimal performance. In this paper, we observe that model predictions can substantially benefit the training process: self-adaptive training significantly mitigates the overfitting issue and improves generalization over ERM under both random and adversarial noise.

The giant formula for simultaneously enlarging the resolution, depth and width provides us aRubik'scube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions.