85 citations · 117 across the 21 of their papers we have counts for
21 papers
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…
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…
Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition
Rohit Gupta, Naveed Akhtar, Gaurav Kumar Nayak +2
Black-box adversarial attacks present a realistic threat to action recognition systems. Existing black-box attacks follow either a query-based approach where an attack is optimized…
Robust Few-shot Learning Without Using any Adversarial Samples
Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri +1
The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime importance. Existing works mainly focus on improving performance on clean data a…
Data-free Defense of Black Box Models Against Adversarial Attacks
Gaurav Kumar Nayak, Inder Khatri, Ruchit Rawal +1
Several companies often safeguard their trained deep models (i.e., details of architecture, learnt weights, training details etc.) from third-party users by exposing them only as b…
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…
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems
Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal +2
Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (rob…
Beyond Classification: Knowledge Distillation using Multi-Object Impressions
Gaurav Kumar Nayak, Monish Keswani, Sharan Seshadri +1
Knowledge Distillation (KD) utilizes training data as a transfer set to transfer knowledge from a complex network (Teacher) to a smaller network (Student). Several works have recen…
Incremental Learning for Animal Pose Estimation using RBF k-DPP
Gaurav Kumar Nayak, Het Shah, Anirban Chakraborty
Pose estimation is the task of locating keypoints for an object of interest in an image. Animal Pose estimation is more challenging than estimating human pose due to high inter and…
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data
Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain +1
Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen…
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…
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images
Gaurav Kumar Nayak, Saksham Jain, R Venkatesh Babu +1
In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery. The price for satellite images depends on the sensor quality and revi…
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…