6 papers
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…
Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning
M Yashwanth, Gaurav Kumar Nayak, Harsh Rangwani +3
Federated Learning (FL) is an emerging machine learning framework that enables multiple clients (coordinated by a server) to collaboratively train a global model by aggregating the…
Open-DeBias: Toward Mitigating Open-Set Bias in Language Models
Arti Rani, Shweta Singh, Nihar Ranjan Sahoo +1
Large Language Models (LLMs) have achieved remarkable success on question answering (QA) tasks, yet they often encode harmful biases that compromise fairness and trustworthiness. M…
MGD: Mode-Guided Dataset Distillation using Diffusion Models
Jeffrey A. Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak +2
Dataset distillation has emerged as an effective strategy, significantly reducing training costs and facilitating more efficient model deployment. Recent advances have leveraged ge…
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID
Nyle Siddiqui, Florinel Alin Croitoru, Gaurav Kumar Nayak +2
With the recent exhibited strength of generative diffusion models, an open research question is if images generated by these models can be used to learn better visual representatio…
CityGuessr: City-Level Video Geo-Localization on a Global Scale
Parth Parag Kulkarni, Gaurav Kumar Nayak, Mubarak Shah
Video geolocalization is a crucial problem in current times. Given just a video, ascertaining where it was captured from can have a plethora of advantages. The problem of worldwide…