Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition

• URL: http://arxiv.org/abs/2209.14385v1
• Date: Wed, 28 Sep 2022 19:21:53 GMT
• Title: Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition
• Authors: Jingyun Jia, Philip K. Chan
• Abstract summary: We use a two-stage training strategy for the open set recognition (OSR) problems. In the first stage, we introduce a self-supervised feature decoupling method that finds the content features of the input samples from the known classes. In the second stage, we fine-tune the content features with the class labels. Our experimental results indicate that our proposed self-supervised approach outperforms others in image and malware OSR problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Assuming unknown classes could be present during classification, the open set recognition (OSR) task aims to classify an instance into a known class or reject it as unknown. In this paper, we use a two-stage training strategy for the OSR problems. In the first stage, we introduce a self-supervised feature decoupling method that finds the content features of the input samples from the known classes. Specifically, our feature decoupling approach learns a representation that can be split into content features and transformation features. In the second stage, we fine-tune the content features with the class labels. The fine-tuned content features are then used for the OSR problems. Moreover, we consider an unsupervised OSR scenario, where we cluster the content features learned from the first stage. To measure representation quality, we introduce intra-inter ratio (IIR). Our experimental results indicate that our proposed self-supervised approach outperforms others in image and malware OSR problems. Also, our analyses indicate that IIR is correlated with OSR performance.

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