6 citations · 7 across the 10 of their papers we have counts for
10 papers
MetaErr: Towards Predicting Error Patterns in Deep Neural Networks
Varun Totakura, Shayok Chakraborty
Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. Unfortunately, deep learning sy…
An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
Varun Totakura, Ankita Singh, Yushun Dong +1
Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a ma…
ACIL: Active Class Incremental Learning for Image Classification
Aditya R. Bhattacharya, Debanjan Goswami, Shayok Chakraborty
Continual learning (or class incremental learning) is a realistic learning scenario for computer vision systems, where deep neural networks are trained on episodic data, and the da…
From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
Lipeng Zu, Yu Qian, Shayok Chakraborty +1
Offline-to-online reinforcement learning (O2O RL) faces a central challenge between retaining offline conservatism and adapting to online feedback under distribution shift. This ch…
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
Chutian Jiang, Hansong Zhou, Xiaonan Zhang +1
Federated learning (FL) enables clients to collaboratively train machine learning models under the coordination of a server in a privacy-preserving manner. One of the main challeng…
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment
Lin Zhang, Linghan Xu, Saman Motamed +2
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled tar…
Active Learning for Video Classification with Frame Level Queries
Debanjan Goswami, Shayok Chakraborty
Deep learning algorithms have pushed the boundaries of computer vision research and have depicted commendable performance in a variety of applications. However, training a robust d…
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel +1
Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce th…
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions. In this paper, we introduce the Nonlin…
Deep Hashing Network for Unsupervised Domain Adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty +1
In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a…