Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection

TL;DR

This paper introduces a transfer learning approach for out-of-distribution detection that leverages a novel image erasing strategy and conditional entropy, enabling effective OOD detection across multiple datasets without retraining.

Contribution

The paper proposes a transferable OOD detection method using conditional entropy and image erasing, eliminating the need for retraining on new in-distribution datasets.

Findings

Achieves comparable performance to state-of-the-art methods

Works across five different datasets without retraining

Utilizes a deep generative model with an erasing strategy

Abstract

Out-of-distribution (OOD) detection is essential to handle the distribution shifts between training and test scenarios. For a new in-distribution (ID) dataset, existing methods require retraining to capture the dataset-specific feature representation or data distribution. In this paper, we propose a deep generative models (DGM) based transferable OOD detection method, which is unnecessary to retrain on a new ID dataset. We design an image erasing strategy to equip exclusive conditional entropy distribution for each ID dataset, which determines the discrepancy of DGM's posteriori ucertainty distribution on different ID datasets. Owing to the powerful representation capacity of convolutional neural networks, the proposed model trained on complex dataset can capture the above discrepancy between ID datasets without retraining and thus achieve transferable OOD detection. We validate the proposed method on five datasets and verity that ours achieves comparable performance to the state-of-the-art group based OOD detection methods that need to be retrained to deploy on new ID datasets. Our code is available at https://github.com/oOHCIOo/CETOOD.