6 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…