11 citations · 17 across the 7 of their papers we have counts for
7 papers · 1 filter
Distilling the Undistillable: Learning from a Nasty Teacher
Surgan Jandial, Yash Khasbage, Arghya Pal +2
The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense effort…
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
Abhinav Java, Shripad Deshmukh, Milan Aggarwal +3
Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text…
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models
Vedant Singh, Surgan Jandial, Ayush Chopra +3
Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. This continues to be a significant are…
SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval
Surgan Jandial, Pinkesh Badjatiya, Pranit Chawla +3
The ability to efficiently search for images is essential for improving the user experiences across various products. Incorporating user feedback, via multi-modal inputs, to naviga…
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Surgan Jandial, Ayush Chopra, Mausoom Sarkar +3
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains. In this work, we introduce a new retrospective loss to improve the tr…
SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
Surgan Jandial, Ayush Chopra, Kumar Ayush +3
Image-based virtual try-on for fashion has gained considerable attention recently. The task requires trying on a clothing item on a target model image. An efficient framework for t…
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…