no code implementations • 19 Apr 2022 • Bilal Alsallakh, Pamela Bhattacharya, Vanessa Feng, Narine Kokhlikyan, Orion Reblitz-Richardson, Rahul Rajan, David Yan
We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets.
no code implementations • NeurIPS Workshop SVRHM 2021 • Bilal Alsallakh, Vivek Miglani, Narine Kokhlikyan, David Adkins, Orion Reblitz-Richardson
When convolutional layers apply no padding, central pixels have more ways to contribute to the convolution than peripheral pixels.
no code implementations • 8 Jun 2021 • Narine Kokhlikyan, Vivek Miglani, Bilal Alsallakh, Miguel Martin, Orion Reblitz-Richardson
Saliency maps have shown to be both useful and misleading for explaining model predictions especially in the context of images.
1 code implementation • 23 Oct 2020 • Vivek Miglani, Narine Kokhlikyan, Bilal Alsallakh, Miguel Martin, Orion Reblitz-Richardson
We explore these effects and find that gradients in saturated regions of this path, where model output changes minimally, contribute disproportionately to the computed attribution.
1 code implementation • ICLR 2021 • Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Jun Yuan, Orion Reblitz-Richardson
We show how feature maps in convolutional networks are susceptible to spatial bias.
2 code implementations • 16 Sep 2020 • Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, Orion Reblitz-Richardson
The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms, also known as feature, neuron and layer importance algorithms, as well as a set of evaluation metrics for these algorithms.