Before revision: We propose two novel loss functions based on Jensen-Shannon divergence for learning under label noise.Following the work of Ghosh et al.(2017) , we argue about their theoretical robustness. Furthermore, we reveal several other desirable properties by drawing informative connections to various loss functions, e.g. , cross entropy , mean absolute error, generalized cross entropy, symmetric cross entropy, label smoothing, and most importantly consistency regularization.
After revision: Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. , cross entropy , mean absolute error, generalized cross entropy, symmetric cross entropy, label smoothing, and most importantly consistency regularization.