55 citations · 328 across the 36 of their papers we have counts for
6 papers · 1 filter
Mitigating Bias: Enhancing Image Classification by Improving Model Explanations
Raha Ahmadi, Mohammad Javad Rajabi, Mohammad Khalooie +1
Deep learning models have demonstrated remarkable capabilities in learning complex patterns and concepts from training data. However, recent findings indicate that these models ten…
Global-Local Processing in Convolutional Neural Networks
Zahra Rezvani, Soroor Shekarizeh, Mohammad Sabokrou
Convolutional Neural Networks (CNNs) have achieved outstanding performance on image processing challenges. Actually, CNNs imitate the typically developed human brain structures at…
Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis
Mohammad Mahdi Mehmanchi, Mahbod Nouri, Mohammad Sabokrou
This paper proposes a straightforward and cost-effective approach to assess whether a deep neural network (DNN) relies on the primary concepts of training samples or simply learns…
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
Mozhgan Pourkeshavarz, Mohammad Sabokrou, Amir Rasouli
In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramo…
A Unified Concept-Based System for Local, Global, and Misclassification Explanations
Fatemeh Aghaeipoor, Dorsa Asgarian, Mohammad Sabokrou
Explainability of Deep Neural Networks (DNNs) has been garnering increasing attention in recent years. Of the various explainability approaches, concept-based techniques stand out…
Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space
Hossein Rezaei, Mohammad Sabokrou
Machine learning models that are overfitted/overtrained are more vulnerable to knowledge leakage, which poses a risk to privacy. Suppose we download or receive a model from a third…