BOOD: Boundary-based Out-Of-Distribution Data Generation

TL;DR

BOOD introduces a novel boundary-based framework that synthesizes high-quality out-of-distribution data using diffusion models, significantly improving OOD detection performance by generating informative and human-compatible outliers.

Contribution

The paper presents BOOD, a new method that efficiently generates OOD features crossing decision boundaries in latent space for better detection.

Findings

BOOD achieves a 29.64% reduction in FPR95 on CIFAR-100.

BOOD improves AUROC by 7.27% over previous methods.

BOOD outperforms state-of-the-art in OOD detection benchmarks.

Abstract

Harnessing the power of diffusion models to synthesize auxiliary training data based on latent space features has proven effective in enhancing out-of-distribution (OOD) detection performance. However, extracting effective features outside the in-distribution (ID) boundary in latent space remains challenging due to the difficulty of identifying decision boundaries between classes. This paper proposes a novel framework called Boundary-based Out-Of-Distribution data generation (BOOD), which synthesizes high-quality OOD features and generates human-compatible outlier images using diffusion models. BOOD first learns a text-conditioned latent feature space from the ID dataset, selects ID features closest to the decision boundary, and perturbs them to cross the decision boundary to form OOD features. These synthetic OOD features are then decoded into images in pixel space by a diffusion model. Compared to previous works, BOOD provides a more training efficient strategy for synthesizing informative OOD features, facilitating clearer distinctions between ID and OOD data. Extensive experimental results on common benchmarks demonstrate that BOOD surpasses the state-of-the-art method significantly, achieving a 29.64% decrease in average FPR95 (40.31% vs. 10.67%) and a 7.27% improvement in average AUROC (90.15% vs. 97.42%) on the CIFAR-100 dataset.