51 citations · 62 across the 8 of their papers we have counts for
9 papers · 1 filter
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning
Soumya Banerjee, Vinay Kumar Verma
Active learning aims to select optimal samples for labeling, minimizing annotation costs. This paper introduces a unified representation learning framework tailored for active lear…
Class Incremental Online Streaming Learning
Soumya Banerjee, Vinay Kumar Verma, Toufiq Parag +2
A wide variety of methods have been developed to enable lifelong learning in conventional deep neural networks. However, to succeed, these methods require a `batch' of samples to b…
Hypernetworks for Continual Semi-Supervised Learning
Dhanajit Brahma, Vinay Kumar Verma, Piyush Rai
Learning from data sequentially arriving, possibly in a non i.i.d. way, with changing task distribution over time is called continual learning. Much of the work thus far in continu…
Knowledge Consolidation based Class Incremental Online Learning with Limited Data
Mohammed Asad Karim, Vinay Kumar Verma, Pravendra Singh +2
We propose a novel approach for class incremental online learning in a limited data setting. This problem setting is challenging because of the following constraints: (1) Classes a…
Efficient Feature Transformations for Discriminative and Generative Continual Learning
Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta +2
As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critica…
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Sakshi Varshney, Vinay Kumar Verma, Srijith P K +2
We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is ba…