Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
- Mengke Li ,
- Yiu-ming Cheung ,
- Yang Lu
Abstract
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 therefore proposes the
Gaussian clouded logit adjustment by Gaussian perturbing 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 the classifier, we accordingly 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.