Curriculum Loss: Robust Learning and Generalization against Label Corruption
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Generalization is vital important for many deep network models. It becomes more challenging when high robustness is required for learning with noisy labels. The 0-1 loss has monotonic relationship between empirical adversary (reweighted) risk, and it is robust to outliers. However, it is also difficult to optimize. To efficiently optimize 0-1 loss while keeping its robust properties, we propose a very simple and efficient loss, i.e. curriculum loss (CL). Our CL is a tighter upper bound of the 0-1 loss compared with conventional summation based surrogate losses. Moreover, CL can adaptively select samples for training as a curriculum learning. To handle large rate of noisy label corruption, we extend our curriculum loss to a more general form that can automatically prune the estimated noisy samples during training. Experimental results on noisy MNIST, CIFAR10 and CIFAR100 dataset validate the robustness of the proposed loss.
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