85 citations · 128 across the 25 of their papers we have counts for
6 papers · 1 filter
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…
CoNMix for Source-free Single and Multi-target Domain Adaptation
Vikash Kumar, Rohit Lal, Himanshu Patil +1
This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Ma…
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers
Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Certified defense using randomized smoothing is a popular technique to provide robustness guarantees for deep neural networks against l2 adversarial attacks. Existing works use thi…
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation
Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty
Knowledge Distillation is an effective method to transfer the learning across deep neural networks. Typically, the dataset originally used for training the Teacher model is chosen…
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier
Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty +1
In this era of digital information explosion, an abundance of data from numerous modalities is being generated as well as archived everyday. However, most problems associated with…
Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj +2
Knowledge distillation deals with the problem of training a smaller model (Student) from a high capacity source model (Teacher) so as to retain most of its performance. Existing ap…