  The existence of label noise imposes significant challenges (e.g., poor
generalization) on the training process of deep neural networks (DNN). As a
remedy, this paper introduces a permutation layer learning approach termed
PermLL to dynamically calibrate the training process of the DNN subject to
instance-dependent and instance-independent label noise. The proposed method
augments the architecture of a conventional DNN by an instance-dependent
permutation layer. This layer is essentially a convex combination of
permutation matrices that is dynamically calibrated for each sample. The
primary objective of the permutation layer is to correct the loss of noisy
samples mitigating the effect of label noise. We provide two variants of PermLL
in this paper: one applies the permutation layer to the model's prediction,
while the other applies it directly to the given noisy label. In addition, we
provide a theoretical comparison between the two variants and show that
previous methods can be seen as one of the variants. Finally, we validate
PermLL experimentally and show that it achieves state-of-the-art performance on
both real and synthetic datasets.
