Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
Mengke Li Yiu-ming Cheung Yang Lu
Long-tailed data is still a big challenge for deep neural networks, even
though they have achieved great success on balanced data. We observe that
vanilla training on long-tailed data with cross-entropy loss makes the
instance-rich head classes severely squeeze the spatial distribution of the
tail classes, which leads to difficulty in classifying tail class samples.
Furthermore, the original cross-entropy loss can only propagate gradient
short-lively because the gradient in softmax form rapidly approaches zero as
the logit difference increases. This phenomenon is called softmax saturation.
It is unfavorable for training on balanced data, but can be utilized to adjust
the validity of the samples in long-tailed data, thereby solving the distorted
embedding space of long-tailed problems. To this end, this paper proposes the
Gaussian clouded logit adjustment by Gaussian perturbation of different class
logits with varied amplitude. We define the amplitude of perturbation as cloud
size and set relatively large cloud sizes to tail classes. The large cloud size
can reduce the softmax saturation and thereby making tail class samples more
active as well as enlarging the embedding space. To alleviate the bias in a
classifier, we therefore propose the class-based effective number sampling
strategy with classifier re-training. Extensive experiments on benchmark
datasets validate the superior performance of the proposed method. Source code
is available at https://github.com/Keke921/GCLLoss.