No One Left Behind: Improving the Worst Categories in Long-Tailed
Learning
- URL: http://arxiv.org/abs/2303.03630v1
- Date: Tue, 7 Mar 2023 03:24:54 GMT
- Title: No One Left Behind: Improving the Worst Categories in Long-Tailed
Learning
- Authors: Yingxiao Du, Jianxin Wu
- Abstract summary: We argue that under such an evaluation setting, some categories are inevitably sacrificed.
We propose a simple plug-in method that is applicable to a wide range of methods.
- Score: 29.89394406438639
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unlike the case when using a balanced training dataset, the per-class recall
(i.e., accuracy) of neural networks trained with an imbalanced dataset are
known to vary a lot from category to category. The convention in long-tailed
recognition is to manually split all categories into three subsets and report
the average accuracy within each subset. We argue that under such an evaluation
setting, some categories are inevitably sacrificed. On one hand, focusing on
the average accuracy on a balanced test set incurs little penalty even if some
worst performing categories have zero accuracy. On the other hand, classes in
the "Few" subset do not necessarily perform worse than those in the "Many" or
"Medium" subsets. We therefore advocate to focus more on improving the lowest
recall among all categories and the harmonic mean of all recall values.
Specifically, we propose a simple plug-in method that is applicable to a wide
range of methods. By simply re-training the classifier of an existing
pre-trained model with our proposed loss function and using an optional
ensemble trick that combines the predictions of the two classifiers, we achieve
a more uniform distribution of recall values across categories, which leads to
a higher harmonic mean accuracy while the (arithmetic) average accuracy is
still high. The effectiveness of our method is justified on widely used
benchmark datasets.
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