Inducing Neural Collapse in Deep Long-tailed Learning
- URL: http://arxiv.org/abs/2302.12453v1
- Date: Fri, 24 Feb 2023 05:07:05 GMT
- Title: Inducing Neural Collapse in Deep Long-tailed Learning
- Authors: Xuantong Liu, Jianfeng Zhang, Tianyang Hu, He Cao, Lujia Pan, Yuan Yao
- Abstract summary: We propose two explicit feature regularization terms to learn high-quality representation for class-imbalanced data.
With the proposed regularization, Neural Collapse phenomena will appear under the class-imbalanced distribution.
Our method is easily implemented, highly effective, and can be plugged into most existing methods.
- Score: 13.242721780822848
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although deep neural networks achieve tremendous success on various
classification tasks, the generalization ability drops sheer when training
datasets exhibit long-tailed distributions. One of the reasons is that the
learned representations (i.e. features) from the imbalanced datasets are less
effective than those from balanced datasets. Specifically, the learned
representation under class-balanced distribution will present the Neural
Collapse (NC) phenomena. NC indicates the features from the same category are
close to each other and from different categories are maximally distant,
showing an optimal linear separable state of classification. However, the
pattern differs on imbalanced datasets and is partially responsible for the
reduced performance of the model. In this work, we propose two explicit feature
regularization terms to learn high-quality representation for class-imbalanced
data. With the proposed regularization, NC phenomena will appear under the
class-imbalanced distribution, and the generalization ability can be
significantly improved. Our method is easily implemented, highly effective, and
can be plugged into most existing methods. The extensive experimental results
on widely-used benchmarks show the effectiveness of our method
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