9 citations · 12 across the 12 of their papers we have counts for
7 papers · 1 filter
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.…
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task
Pratik Mazumder, Pravendra Singh, Vinay P. Namboodiri
In this paper, we propose an approach to improve few-shot classification performance using a composite rotation based auxiliary task. Few-shot classification methods aim to produce…
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…
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings
Pratik Mazumder, Pravendra Singh, Kranti Kumar Parida +1
In this paper, we propose a novel approach for generalized zero-shot learning in a multi-modal setting, where we have novel classes of audio/video during testing that are not seen…
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…
Cooperative Initialization based Deep Neural Network Training
Pravendra Singh, Munender Varshney, Vinay P. Namboodiri
Researchers have proposed various activation functions. These activation functions help the deep network to learn non-linear behavior with a significant effect on training dynamics…