Search Results for author: Hossein Mirzaei

Found 5 papers, 4 papers with code

Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings

1 code implementation14 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

Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework

no code implementations15 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.

Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection

1 code implementation28 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)

Anomaly Detection Novelty Detection

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

1 code implementation26 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.

Anomaly Detection Novelty Detection +3

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