1 citations · 1 across the 2 of their papers we have counts for
14 papers
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…
RNNP: A Robust Few-Shot Learning Approach
Pratik Mazumder, Pravendra Singh, Vinay P. Namboodiri
Learning from a few examples is an important practical aspect of training classifiers. Various works have examined this aspect quite well. However, all existing approaches assume t…
Passive Batch Injection Training Technique: Boosting Network Performance by Injecting Mini-Batches from a different Data Distribution
Pravendra Singh, Pratik Mazumder, Vinay P. Namboodiri
This work presents a novel training technique for deep neural networks that makes use of additional data from a distribution that is different from that of the original input data.…
Minimizing Supervision in Multi-label Categorization
Rajat, Munender Varshney, Pravendra Singh +1
Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In…
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…