Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection

TL;DR

This paper introduces a novel Adaptive Hierarchical Graph Cut network (AHGC) for out-of-distribution detection that leverages hierarchical graph structures and label granularity differences to improve detection accuracy on challenging benchmarks.

Contribution

The paper proposes a new hierarchical graph-based method that considers label granularity differences for more effective out-of-distribution detection.

Findings

AHGC outperforms state-of-the-art methods by up to 81.24% in FPR95 on CIFAR-100.

The method effectively handles datasets with different label granularities.

Extensive experiments validate the superiority of AHGC on CIFAR benchmarks.

Abstract

This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so as to prevent models trained on in-distribution (ID) data from producing unreliable predictions. Although previous works have made decent success, they are ineffective for real-world challenging applications since these methods simply regard all unlabeled data as OOD data and ignore the case that different datasets have different label granularity. For example, "cat" on CIFAR-10 and "tabby cat" on Tiny-ImageNet share the same semantics but have different labels due to various label granularity. To this end, in this paper, we propose a novel Adaptive Hierarchical Graph Cut network (AHGC) to deeply explore the semantic relationship between different images. Specifically, we construct a hierarchical KNN graph to evaluate the similarities between different images based on the cosine similarity. Based on the linkage and density information of the graph, we cut the graph into multiple subgraphs to integrate these semantics-similar samples. If the labeled percentage in a subgraph is larger than a threshold, we will assign the label with the highest percentage to unlabeled images. To further improve the model generalization, we augment each image into two augmentation versions, and maximize the similarity between the two versions. Finally, we leverage the similarity score for OOD detection. Extensive experiments on two challenging benchmarks (CIFAR- 10 and CIFAR-100) illustrate that in representative cases, AHGC outperforms state-of-the-art OOD detection methods by 81.24% on CIFAR-100 and by 40.47% on CIFAR-10 in terms of "FPR95", which shows the effectiveness of our AHGC.