Your Out-of-Distribution Detection Method is Not Robust!
arxiv.org · 8,009 words · saved by 1 readers
N/A
Your Out-of-Distribution Detection Method is Not Robust! Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, Mohammad Hossein Rohban arXiv:2209.15246v1 [cs.CV] 30 Sep 2022 Department of Computer Engineering Sharif University of…
saved by
related reading
- Deep Anomaly Detection with Outlier Exposurearxiv.org
- [2003.01690] Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacksarxiv.org
- [1911.08731] Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationarxiv.org
- https://arxiv.org/pdf/2006.11239arxiv.org
- On Getting Confidence Estimates from Neural Networks | Bharath's notesbharathpbhat.github.io
- [1909.02060] Distributionally Robust Language Modelingarxiv.org
- openaccess.thecvf.com/content/ICCV2023/papers/Li_Your_Diffusion_Model_is_Secretly_a_Zero-Shot_Classifier_ICCV_2023_paper.pdfopenaccess.thecvf.com
- Solving adversarial attacks in computer vision as a baby version of general AI alignment | Stanislav Fortstanislavfort.com
- Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer visionarxiv.org
- [2404.03348] Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanationsarxiv.org
- Elucidating the Design Space of Diffusion-Based Generative Models | PDFarxiv.org
- FREQUENCY MASKING FOR UNIVERSAL DEEPFAKE DETECTIONarxiv.org