1 code implementation • 14 Oct 2024 • Hossein Mirzaei, Mackenzie W. Mathis
By incorporating a tailored loss function, we apply Lyapunov stability theory to ensure that both in-distribution (ID) and OOD data converge to stable equilibrium points within the dynamical system.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • CVPR 2024 • Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi, Mohammad Sabokrou, Mohammad Hossein Rohban
More precisely, for novelty detection, distribution shifts may occur in the training set or the test set.
no code implementations • 15 Oct 2023 • Arshia Soltani Moakhar, Mohammad Azizmalayeri, Hossein Mirzaei, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues.
1 code implementation • 28 May 2022 • Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves, Cees G. M. Snoek, Mohammad Sabokrou, Mohammad Hossein Rohban
Effectiveness of our method for both the near-distribution and standard novelty detection is assessed through extensive experiments on datasets in diverse applications such as medical images, object classification, and quality control.
Ranked #3 on Anomaly Detection on One-class CIFAR-10 (using extra training data)
1 code implementation • 26 Oct 2021 • Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou
To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection.