A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and ... Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks et al. openreview.net/forum?id=aRtjV… #NewPaper #PaperPost

Our paper titled as "Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection" has been accepted for presentation at the ICLR 2023. This is a joint work with HOSSEIN, mrz.salehi, Sajjad Shahabi, Efstratios Gavves, Cees Snoek, Mohammad Sabokrou.

I am thrilled that our work on robust anomaly detection has been accepted in #ICML2024. TL;DR: We found that synthetic outliers with two key properties make this happen: near-OODness and diversity. 1/

September means it’s the labs 7th anniversary 🤯🔥🥂🎉🍾… And we welcome three new amazing PhD students: Hossein Mirzaei HOSSEIN , Xiaohang Yu, and Ti Wang Ti ! All brilliant computer scientists ready to work on the next generation of ML & CV for Science 🔥🧠🦄

🚨adversarial robustness is becoming even more critical as AI systems are deployed in the real-world, but how can we detect outliers (adversarials) without having trained on them 👀?  In our new preprint, we introduce AROS💍: It leverages neural ODEs and Lyapunov stability

Downloaded a model's weights and wondering whether it has been poisoned or not? Meet our method, TRODO (Trojan scanning by Detection of Adversarial shift in Out-of-Distribution samples), accepted in #NeurIPS 2024. 1/ Paper: neurips.cc/virtual/2024/p… Code: github.com/rohban-lab/TRO…

🔥🙏🏼 #AROS 💍 is accepted to #ICLR2025 ICLR 2026 ! So proud of HOSSEIN - what an awesome way to kick off his first grad school project 👌👌 Check out the updated arXiv version of the paper, open code (including python package) below ⬇️ #AROS💍 leverages neural ODEs and

🎉 NeCo is accepted to ICLR 2026 #ICLR25 TL;DR: Self-supervised patch-level neighbor consistency boosts dense features of foundation models like DINOv2—in just 19 GPU hours. 📍 Tomorrow,10:00–12:30@Hall3+Hall2B#133 📄 Paper: openreview.net/forum?id=Qro97…

🚀 Excited to share our #ICLR2025 paper! 🤖 AROS: Adversarially Robust Out-of-Distribution Detection through Stability 📄 Paper:openreview.net/forum?id=GrDne… 🔹 #AI_safety 🔹 #Trustworthy_ML 🗓️ Poster Session: Fri 25 Apr 10-12:30 📍 Hall3+Hall 2B (#327)