activity
20172022
most citedCaptum: A unified and generic model interpretability library for PyTorch

649 citations · 909 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CV20221 cited

A Tour of Visualization Techniques for Computer Vision Datasets

Bilal Alsallakh, Pamela Bhattacharya, Vanessa Feng +4

We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets. These techniques help us understand properties and latent patterns in such data, by…

cs.CV2020

Investigating Saturation Effects in Integrated Gradients

Vivek Miglani, Narine Kokhlikyan, Bilal Alsallakh +2

Integrated Gradients has become a popular method for post-hoc model interpretability. De-spite its popularity, the composition and relative impact of different regions of the integ…

cs.CV202032 cited

Mind the Pad -- CNNs can Develop Blind Spots

Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani +2

We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain locations is systematic…

cs.CV2020

Visualizing Classification Structure of Large-Scale Classifiers

Bilal Alsallakh, Zhixin Yan, Shabnam Ghaffarzadegan +2

We propose a measure to compute class similarity in large-scale classification based on prediction scores. Such measure has not been formally pro-posed in the literature. We show h…

cs.CV2017211 cited

Do Convolutional Neural Networks Learn Class Hierarchy?

Bilal Alsallakh, Amin Jourabloo, Mao Ye +2

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possib…