85 citations · 117 across the 21 of their papers we have counts for
4 papers · 1 filter
DAD++: Improved Data-free Test Time Adversarial Defense
Gaurav Kumar Nayak, Inder Khatri, Shubham Randive +2
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness h…
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
Vicente Vivanco Cepeda, Gaurav Kumar Nayak, Mubarak Shah
Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. This task has considerable challenges due to immense variation in geographic lan…
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks
Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak +2
Federated learning is a promising direction to tackle the privacy issues related to sharing patients' sensitive data. Often, federated systems in the medical image analysis domain…
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
M Yashwanth, Gaurav Kumar Nayak, Arya Singh +2
Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local trai…