Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer

• URL: http://arxiv.org/abs/2405.01662v1
• Date: Thu, 2 May 2024 18:33:02 GMT
• Title: Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layer
• Authors: Qiuyu Zhu, Yiwei He,
• Abstract summary: This paper concentrates on the high-dimensional features output by the final convolutional layer, which contain rich image features. Our key idea is to project these high-dimensional features into two specific feature subspaces, trained with Predefined Evenly-Distribution Class Centroids (PEDCC)-Loss. Our method requires only the training of the classification network model, eschewing any need for input pre-processing or specific OOD data pre-tuning.
• Score: 5.902332693463877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) detection, crucial for reliable pattern classification, discerns whether a sample originates outside the training distribution. This paper concentrates on the high-dimensional features output by the final convolutional layer, which contain rich image features. Our key idea is to project these high-dimensional features into two specific feature subspaces, leveraging the dimensionality reduction capacity of the network's linear layers, trained with Predefined Evenly-Distribution Class Centroids (PEDCC)-Loss. This involves calculating the cosines of three projection angles and the norm values of features, thereby identifying distinctive information for in-distribution (ID) and OOD data, which assists in OOD detection. Building upon this, we have modified the batch normalization (BN) and ReLU layer preceding the fully connected layer, diminishing their impact on the output feature distributions and thereby widening the distribution gap between ID and OOD data features. Our method requires only the training of the classification network model, eschewing any need for input pre-processing or specific OOD data pre-tuning. Extensive experiments on several benchmark datasets demonstrates that our approach delivers state-of-the-art performance. Our code is available at https://github.com/Hewell0/ProjOOD.

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