1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2022★ 1 cited
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
Amil Dravid, Florian Schiffers, Boqing Gong +1
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space…
cs.CV2022
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Amil Dravid, Florian Schiffers, Yunan Wu +2
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But th…
cs.CV2021★ 1 cited
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Amil Dravid, Aggelos K. Katsaggelos
Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice. Popular techniques such as Gradient-…