InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning

• URL: http://arxiv.org/abs/2303.07269v1
• Date: Mon, 13 Mar 2023 16:45:41 GMT
• Title: InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning
• Authors: Zhuoran Yu, Yin Li, Yong Jae Lee
• Abstract summary: We present a new perspective of pseudo-labeling for imbalanced semi-supervised learning (SSL) We measure whether an unlabeled sample is likely to be in-distribution'' or out-of-distribution'' Experiments demonstrate that our energy-based pseudo-labeling method, textbfInPL, significantly outperforms confidence-based methods on imbalanced SSL benchmarks.
• Score: 34.062061310242385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent state-of-the-art methods in imbalanced semi-supervised learning (SSL) rely on confidence-based pseudo-labeling with consistency regularization. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable. In this work, we present a new perspective of pseudo-labeling for imbalanced SSL. Without relying on model confidence, we propose to measure whether an unlabeled sample is likely to be ``in-distribution''; i.e., close to the current training data. To decide whether an unlabeled sample is ``in-distribution'' or ``out-of-distribution'', we adopt the energy score from out-of-distribution detection literature. As training progresses and more unlabeled samples become in-distribution and contribute to training, the combined labeled and pseudo-labeled data can better approximate the true class distribution to improve the model. Experiments demonstrate that our energy-based pseudo-labeling method, \textbf{InPL}, albeit conceptually simple, significantly outperforms confidence-based methods on imbalanced SSL benchmarks. For example, it produces around 3\% absolute accuracy improvement on CIFAR10-LT. When combined with state-of-the-art long-tailed SSL methods, further improvements are attained. In particular, in one of the most challenging scenarios, InPL achieves a 6.9\% accuracy improvement over the best competitor.

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