  Features, logits, and labels are the three primary data when a sample passes
through a deep neural network. Feature perturbation and label perturbation
receive increasing attention in recent years. They have been proven to be
useful in various deep learning approaches. For example, (adversarial) feature
perturbation can improve the robustness or even generalization capability of
learned models. However, limited studies have explicitly explored for the
perturbation of logit vectors. This work discusses several existing methods
related to class-level logit perturbation. A unified viewpoint between
positive/negative data augmentation and loss variations incurred by logit
perturbation is established. A theoretical analysis is provided to illuminate
why class-level logit perturbation is useful. Accordingly, new methodologies
are proposed to explicitly learn to perturb logits for both single-label and
multi-label classification tasks. Extensive experiments on benchmark image
classification data sets and their long-tail versions indicated the competitive
performance of our learning method. As it only perturbs on logit, it can be
used as a plug-in to fuse with any existing classification algorithms. All the
codes are available at https://github.com/limengyang1992/lpl.
