Search Results for author: Vinay Kumar Verma

Found 22 papers, 6 papers with code

Streaming LifeLong Learning With Any-Time Inference

no code implementations27 Jan 2023 Soumya Banerjee, Vinay Kumar Verma, Vinay P. Namboodiri

Despite rapid advancements in lifelong learning (LLL) research, a large body of research mainly focuses on improving the performance in the existing \textit{static} continual learning (CL) setups.

Continual Learning Management

Pushing the Efficiency Limit Using Structured Sparse Convolutions

no code implementations23 Oct 2022 Vinay Kumar Verma, Nikhil Mehta, Shijing Si, Ricardo Henao, Lawrence Carin

Weight pruning is among the most popular approaches for compressing deep convolutional neural networks.

Class Incremental Online Streaming Learning

no code implementations20 Oct 2021 Soumya Banerjee, Vinay Kumar Verma, Toufiq Parag, Maneesh Singh, Vinay P. Namboodiri

We propose a novel approach (CIOSL) for the class-incremental learning in an \emph{online streaming setting} to address these challenges.

Class Incremental Learning Incremental Learning +1

Hypernetworks for Continual Semi-Supervised Learning

no code implementations5 Oct 2021 Dhanajit Brahma, Vinay Kumar Verma, Piyush Rai

Further, we present $\textit{Semi-Split CIFAR-10}$, a new benchmark for continual semi-supervised learning, obtained by modifying the $\textit{Split CIFAR-10}$ dataset, in which the tasks with labelled and unlabelled data arrive sequentially.

Continual Learning Split-CIFAR-10

Knowledge Consolidation based Class Incremental Online Learning with Limited Data

no code implementations12 Jun 2021 Mohammed Asad Karim, Vinay Kumar Verma, Pravendra Singh, Vinay Namboodiri, Piyush Rai

In our approach, we learn robust representations that are generalizable across tasks without suffering from the problems of catastrophic forgetting and overfitting to accommodate future classes with limited samples.

Class Incremental Learning Incremental Learning +1

Efficient Feature Transformations for Discriminative and Generative Continual Learning

1 code implementation CVPR 2021 Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta, Piyush Rai, Lawrence Carin

However, the growth in the number of additional parameters of many of these types of methods can be computationally expensive at larger scales, at times prohibitively so.

Continual Learning

Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning

no code implementations23 Feb 2021 Vinay Kumar Verma, Kevin Liang, Nikhil Mehta, Lawrence Carin

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 remain.

Generalized Zero-Shot Learning Meta-Learning

Calibrating CNNs for Lifelong Learning

no code implementations NeurIPS 2020 Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai

Further, our approach does not require storing data samples from the old tasks, which is done by many replay based methods.

Continual Learning

Towards Zero-Shot Learning with Fewer Seen Class Examples

no code implementations14 Nov 2020 Vinay Kumar Verma, Ashish Mishra, Anubha Pandey, Hema A. Murthy, Piyush Rai

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 very few.

Meta-Learning Zero-Shot Learning

ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions

1 code implementation23 Jul 2020 Anurag Roy, Vinay Kumar Verma, Kripabandhu Ghosh, Saptarshi Ghosh

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 mode of the documents (e. g., images) from a given training set.

Cross-Modal Information Retrieval Image Retrieval +4

Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval

no code implementations18 Jan 2020 Anubha Pandey, Ashish Mishra, Vinay Kumar Verma, Anurag Mittal, Hema A. Murthy

Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training.

Retrieval Sketch-Based Image Retrieval

A "Network Pruning Network" Approach to Deep Model Compression

no code implementations15 Jan 2020 Vinay Kumar Verma, Pravendra Singh, Vinay P. Namboodiri, Piyush Rai

The pruner is essentially a multitask deep neural network with binary outputs that help identify the filters from each layer of the original network that do not have any significant contribution to the model and can therefore be pruned.

Knowledge Distillation Model Compression +4

A Meta-Learning Framework for Generalized Zero-Shot Learning

1 code implementation10 Sep 2019 Vinay Kumar Verma, Dhanajit Brahma, Piyush Rai

Our proposed model yields significant improvements on standard ZSL as well as more challenging GZSL setting.

Generalized Zero-Shot Learning Meta-Learning

Play and Prune: Adaptive Filter Pruning for Deep Model Compression

1 code implementation11 May 2019 Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri

Our framework, called Play and Prune (PP), jointly prunes and fine-tunes CNN model parameters, with an adaptive pruning rate, while maintaining the model's predictive performance.

Model Compression

Generative Model for Zero-Shot Sketch-Based Image Retrieval

no code implementations18 Apr 2019 Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra, Piyush Rai

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.

Image Generation Retrieval +1

HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs

1 code implementation CVPR 2019 Pravendra Singh, Vinay Kumar Verma, Piyush Rai, Vinay P. Namboodiri

We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels.

A Generative Approach to Zero-Shot and Few-Shot Action Recognition

no code implementations27 Jan 2018 Ashish Mishra, Vinay Kumar Verma, M Shiva Krishna Reddy, Arulkumar S, Piyush Rai, Anurag Mittal

In particular, we assume that the distribution parameters for any action class in the visual space can be expressed as a linear combination of a set of basis vectors where the combination weights are given by the attributes of the action class.

Few-Shot action recognition Few Shot Action Recognition +3

Generalized Zero-Shot Learning via Synthesized Examples

no code implementations CVPR 2018 Vinay Kumar Verma, Gundeep Arora, Ashish Mishra, Piyush Rai

Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings.

General Classification Generalized Zero-Shot Learning

A Simple Exponential Family Framework for Zero-Shot Learning

2 code implementations25 Jul 2017 Vinay Kumar Verma, Piyush Rai

We model each class-conditional distribution as an exponential family distribution and the parameters of the distribution of each seen/unseen class are defined as functions of the respective observed class attributes.

Few-Shot Learning Zero-Shot Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.