SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation
<|reference_start|>SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation: Out-of-distribution (OOD) detection is crucial for the safe deployment of neural networks. Existing CLIP-based approaches perform OOD detection by devising novel scoring functions or sophisticated fine-tuning methods. In this work, we propose SeTAR, a novel, training-free OOD detection method that leverages selective low-rank approximation of weight matrices in vision-language and vision-only models. SeTAR enhances OOD detection via post-hoc modification of the model's weight matrices using a simple greedy search algorithm. Based on SeTAR, we further propose SeTAR+FT, a fine-tuning extension optimizing model performance for OOD detection tasks. Extensive evaluations on ImageNet1K and Pascal-VOC benchmarks show SeTAR's superior performance, reducing the false positive rate by up to 18.95% and 36.80% compared to zero-shot and fine-tuning baselines. Ablation studies further validate our approach's effectiveness, robustness, and generalizability across different model backbones. Our work offers a scalable, efficient solution for OOD detection, setting a new state-of-the-art in this area.<|reference_end|>
@article{li2024setar:,
    title={SeTAR: Out-of-Distribution Detection with Selective Low-Rank
  Approximation},
    author={Yixia Li, Boya Xiong, Guanhua Chen, Yun Chen},
    journal={arXiv preprint arXiv:2406.12629},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12629},
    primaryClass={cs.CL cs.AI cs.CV}
}