51 citations · 62 across the 8 of their papers we have counts for
11 papers · 1 filter
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si +2
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, the…
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
Vinay Kumar Verma, Kevin Liang, Nikhil Mehta +1
Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still…
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…
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
Anurag Roy, Vinay Kumar Verma, Kripabandhu Ghosh +1
Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.g., text) to the m…
Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval
Anubha Pandey, Ashish Mishra, Vinay Kumar Verma +2
Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training. The assumption may not always be practical sin…
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…