Long-tailed Distribution Adaptation.
2021
Recognizing images with long-tailed distributions remains a challenging
problem while there lacks an interpretable mechanism to solve this problem. In
this study, we formulate Long-tailed recognition as Domain Adaption (LDA), by
modeling the long-tailed distribution as an unbalanced domain and the general
distribution as a balanced domain. Within the balanced domain, we propose to
slack the generalization error bound, which is defined upon the empirical risks
of unbalanced and balanced domains and the divergence between them. We propose
to jointly optimize empirical risks of the unbalanced and balanced domains and
approximate their domain divergence by intra-class and inter-class distances,
with the aim to adapt models trained on the long-tailed distribution to general
distributions in an interpretable way. Experiments on benchmark datasets for
image recognition, object detection, and instance segmentation validate that
our LDA approach, beyond its interpretability, achieves state-of-the-art
performance. Code is available at https://github.com/pengzhiliang/LDA.
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