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Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition. (arXiv:2107.09249v4 [cs.CV] UPDATED)
Oct. 28, 2022, 1:15 a.m. | Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng
cs.CV updates on arXiv.org arxiv.org
Existing long-tailed recognition methods, aiming to train class-balanced
models from long-tailed data, generally assume the models would be evaluated on
the uniform test class distribution. However, practical test class
distributions often violate this assumption (e.g., being either long-tailed or
even inversely long-tailed), which may lead existing methods to fail in real
applications. In this paper, we study a more practical yet challenging task,
called test-agnostic long-tailed recognition, where the training class
distribution is long-tailed while the test class distribution is …
More from arxiv.org / cs.CV updates on arXiv.org
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