134 citations · 459 across the 38 of their papers we have counts for
13 papers · 1 filter
NOVO: Unlearning-Compliant Vision Transformers
Soumya Roy, Soumya Banerjee, Vinay Verma +3
Machine unlearning (MUL) refers to the problem of making a pre-trained model selectively forget some training instances or class(es) while retaining performance on the remaining da…
Novel Class Discovery without Forgetting
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. I…
Spacing Loss for Discovering Novel Categories
K J Joseph, Sujoy Paul, Gaurav Aggarwal +4
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances fro…
SITA: Single Image Test-time Adaptation
Ansh Khurana, Sujoy Paul, Piyush Rai +2
In Test-time Adaptation (TTA), given a source model, the goal is to adapt it to make better predictions for test instances from a different distribution than the source. Crucially,…
Rectification-based Knowledge Retention for Continual Learning
Pravendra Singh, Pratik Mazumder, Piyush Rai +1
Deep learning models suffer from catastrophic forgetting when trained in an incremental learning setting. In this work, we propose a novel approach to address the task incremental…
Few-Shot Lifelong Learning
Pratik Mazumder, Pravendra Singh, Piyush Rai
Many real-world classification problems often have classes with very few labeled training samples. Moreover, all possible classes may not be initially available for training, and m…
Towards Zero-Shot Learning with Fewer Seen Class Examples
Vinay Kumar Verma, Ashish Mishra, Anubha Pandey +2
We present a meta-learning based generative model for zero-shot learning (ZSL) towards a challenging setting when the number of training examples from each \emph{seen} class is ver…
A "Network Pruning Network" Approach to Deep Model Compression
Vinay Kumar Verma, Pravendra Singh, Vinay P. Namboodiri +1
We present a filter pruning approach for deep model compression, using a multitask network. Our approach is based on learning a a pruner network to prune a pre-trained target netwo…
Play and Prune: Adaptive Filter Pruning for Deep Model Compression
Pravendra Singh, Vinay Kumar Verma, Piyush Rai +1
While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters…
Generative Model for Zero-Shot Sketch-Based Image Retrieval
Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra +1
We present a probabilistic model for Sketch-Based Image Retrieval (SBIR) where, at retrieval time, we are given sketches from novel classes, that were not present at training time.…
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs
Pravendra Singh, Vinay Kumar Verma, Piyush Rai +1
We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based Convolution) redu…
Leveraging Filter Correlations for Deep Model Compression
Pravendra Singh, Vinay Kumar Verma, Piyush Rai +1
We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairw…
A Generative Approach to Zero-Shot and Few-Shot Action Recognition
Ashish Mishra, Vinay Kumar Verma, M Shiva Krishna Reddy +3
We present a generative framework for zero-shot action recognition where some of the possible action classes do not occur in the training data. Our approach is based on modeling ea…