11 citations · 14 across the 5 of their papers we have counts for
5 papers
Decafs: Disentangled Conditional adversarial Flows
Anirudh jain, Sakshi Varshney, Samuel Kaski +1
Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particul…
Shortcut Invariance: Targeted Jacobian Regularization in Disentangled Latent Space
Shivam Pal, Sakshi Varshney, Piyush Rai
Deep neural networks are prone to learning shortcuts, spurious correlations present in the training data that undermine out-of-distribution (OOD) generalization. Most prior work mi…
Continual Learning with Dependency Preserving Hypernetworks
Dupati Srikar Chandra, Sakshi Varshney, P. K. Srijith +1
Humans learn continually throughout their lifespan by accumulating diverse knowledge and fine-tuning it for future tasks. When presented with a similar goal, neural networks suffer…
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Sakshi Varshney, Vinay Kumar Verma, Srijith P K +2
We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is ba…
AdvGAN++ : Harnessing latent layers for adversary generation
Puneet Mangla, Surgan Jandial, Sakshi Varshney +1
Adversarial examples are fabricated examples, indistinguishable from the original image that mislead neural networks and drastically lower their performance. Recently proposed AdvG…