Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization

Autor:	Wang, Haoliang, Zhao, Chen, Chen, Feng
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during training (open-set recognition). However, most existing approaches tackle these issues separately, limiting their practical applicability. To overcome this limitation, we propose a unified framework for open-set domain generalization by introducing Feature-space Semantic Invariance (FSI). FSI maintains semantic consistency across different domains within the feature space, enabling more accurate detection of OOD instances in unseen domains. Additionally, we adopt a generative model to produce synthetic data with novel domain styles or class labels, enhancing model robustness. Initial experiments show that our method improves AUROC by 9.1% to 18.9% on ColoredMNIST, while also significantly increasing in-distribution classification accuracy. Comment: IEEE BigData 2024, Ph.D. Forum
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.07392 Zobrazit plný text záznamu View this record from Arxiv