1 citations · 1 across the 3 of their papers we have counts for
5 papers
Improving Adversarial Robustness of Attribution via Implicit Regularization
Amir Mehrpanah, Matteo Gamba, Hossein Azizpour
The adversarial robustness of attributions is a fundamental requirement for reliable explainability in deep learning, yet existing approaches typically rely on computationally expe…
On Spectral Properties of Gradient-based Explanation Methods
Amir Mehrpanah, Erik Englesson, Hossein Azizpour
Understanding the behavior of deep networks is crucial to increase our confidence in their results. Despite an extensive body of work for explaining their predictions, researchers…
On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations
Amir Mehrpanah, Matteo Gamba, Kevin Smith +1
ReLU networks, while prevalent for visual data, have sharp transitions, sometimes relying on individual pixels for predictions, making vanilla gradient-based explanations noisy and…
Logistic-Normal Likelihoods for Heteroscedastic Label Noise
Erik Englesson, Amir Mehrpanah, Hossein Azizpour
A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters c…
LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning
Fatemeh Karimi, Amir Mehrpanah, Reza Rawassizadeh
Advances in neural networks enable tackling complex computer vision tasks such as depth estimation of outdoor scenes at unprecedented accuracy. Promising research has been done on…