6 citations · 12 across the 10 of their papers we have counts for
10 papers
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi +7
In recent years, there have been significant improvements in various forms of image outlier detection. However, outlier detection performance under adversarial settings lags far be…
Universal Novelty Detection Through Adaptive Contrastive Learning
Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari +5
Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality, which can be interpre…
Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie +4
Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliabilit…
Blacksmith: Fast Adversarial Training of Vision Transformers via a Mixture of Single-step and Multi-step Methods
Mahdi Salmani, Alireza Dehghanpour Farashah, Mohammad Azizmalayeri +4
Despite the remarkable success achieved by deep learning algorithms in various domains, such as computer vision, they remain vulnerable to adversarial perturbations. Adversarial Tr…
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
Arshia Soltani Moakhar, Mohammad Azizmalayeri, Hossein Mirzaei +2
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentra…
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
Mohammad Azizmalayeri, Arman Zarei, Alireza Isavand +2
Current machine learning models achieve super-human performance in many real-world applications. Still, they are susceptible against imperceptible adversarial perturbations. The mo…
Your Out-of-Distribution Detection Method is Not Robust!
Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei +3
Out-of-distribution (OOD) detection has recently gained substantial attention due to the importance of identifying out-of-domain samples in reliability and safety. Although OOD det…
PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations
Simin Shekarpaz, Mohammad Azizmalayeri, Mohammad Hossein Rohban
In this paper, we propose the physics informed adversarial training (PIAT) of neural networks for solving nonlinear differential equations (NDE). It is well-known that the standard…
OOD Augmentation May Be at Odds with Open-Set Recognition
Mohammad Azizmalayeri, Mohammad Hossein Rohban
Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem. There has been a burst of interest in…
Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training
Mohammad Azizmalayeri, Mohammad Hossein Rohban
Recent improvements in deep learning models and their practical applications have raised concerns about the robustness of these models against adversarial examples. Adversarial tra…