Discriminability-Driven Channel Selection for Out-of-Distribution Detection

Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection, working to mitigate overconfident predictions of OOD data. These techniques rectifying anomalous activations, enhancing the distinguishability between in-distribution (ID) data and OOD data. However, they assume by default that every channel is necessary for OOD detection, and rectify anomalous activations in each channel. Empirical evidence has shown that there is a significant difference among various channels in OOD detection, and discarding some channels can greatly enhance the performance of OOD detection. Based on this insight, we propose Discriminability-Driven Channel Selection (DDCS), which leverages an adaptive channel selection by estimating the discriminative score of each channel to boost OOD detection. The discriminative score takes inter-class similarity and inter-class variance of training data into account. However, the estimation of discriminative score itself is susceptible to anomalous activations. To better estimate score, we pre-rectify anomalous activations for each channel mildly. The experimental results show that DDCS achieves state-of-the-art performance on CIFAR and ImageNet-1K benchmarks. Moreover, DDCS can generalize to different backbones and OOD scores.