Web: http://arxiv.org/abs/2206.10843

Sept. 16, 2022, 1:15 a.m. | Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, Suha Kwak

cs.CV updates on arXiv.org arxiv.org

Neural networks are prone to be biased towards spurious correlations between
classes and latent attributes exhibited in a major portion of training data,
which ruins their generalization capability. This paper proposes a new method
for training debiased classifiers with no spurious attribute label. The key
idea of the method is to employ a committee of classifiers as an auxiliary
module that identifies bias-conflicting data, i.e., data without spurious
correlations, and assigns large weights to them when training the main
classifier. …


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