Author name cluster

Vinay Kumar Verma

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8 papers

2 author rows

ICRA Conference 2024 Conference Paper

VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference

Soumya Banerjee
Vinay Kumar Verma
Avideep Mukherjee
Deepak Gupta
Vinay P. Namboodiri
Piyush Rai

Lifelong learning or continual learning is the problem of training an AI agent continuously while also preventing it from forgetting its previously acquired knowledge. Streaming lifelong learning is a challenging setting of lifelong learning with the goal of continuous learning in a dynamic non-stationary environment without forgetting. We introduce a novel approach to lifelong learning, which is streaming (observes each training example only once), requires a single pass over the data, can learn in a class-incremental manner, and can be evaluated on-the-fly (anytime inference). To accomplish these, we propose a novel virtual gradients based approach for continual representation learning which adapts to each new example while also generalizing well on past data to prevent catastrophic forgetting. Our approach also leverages an exponential-moving-average-based semantic memory to further enhance performance. Experiments on diverse datasets with temporally correlated observations demonstrate our method’s efficacy and superior performance over existing methods.

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ICRA Conference 2023 Conference Paper

Streaming LifeLong Learning With Any-Time Inference

Soumya Banerjee
Vinay Kumar Verma
Vinay P. Namboodiri

Despite rapid advancements in the lifelong learning (LL) research, a large body of research mainly focuses on improving the performance in the existing static continual learning (CL) setups. These methods lack the ability to succeed in a rapidly changing dynamic environment, where an AI agent needs to quickly learn new instances in a ‘single pass' from the non-i. i. d (also possibly temporally contiguous/coherent) data streams without suffering from catastrophic forgetting. For practical applicability, we propose a novel lifelong learning approach, which is streaming, i. e. , a single input sample arrives in each time step. Moreover, the proposed approach is single pass, class-incremental, and is subject to be evaluated at any moment. To address this challenging setup and various evaluation protocols, we propose a Bayesian framework, that enables fast parameter update, given a single training example, and enables any-time inference. We additionally propose an implicit regularizer in the form of snap-shot self-distillation, which effectively minimizes the forgetting further. We further propose an effective method that efficiently selects a subset of samples for online memory rehearsal and employs a new replay buffer management scheme that significantly boosts the overall performance. Our empirical evaluations and ablations demonstrate that the proposed method outperforms the prior works by large margins.

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NeurIPS Conference 2021 Conference Paper

CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks

Sakshi Varshney
Vinay Kumar Verma
P. K. Srijith
Lawrence Carin
Piyush Rai

We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is based on learning a set of global and task-specific parameters. The global parameters are fixed across tasks whereas the task-specific parameters act as local adapters for each task, and help in efficiently obtaining task-specific feature maps. Moreover, we propose an element-wise addition of residual bias in the transformed feature space, which further helps stabilize GAN training in such settings. Our approach also leverages task similarities based on the Fisher information matrix. Leveraging this knowledge from previous tasks significantly improves the model performance. In addition, the similarity measure also helps reduce the parameter growth in continual adaptation and helps to learn a compact model. In contrast to the recent approaches for continually-learned GANs, the proposed approach provides a memory-efficient way to perform effective continual data generation. Through extensive experiments on challenging and diverse datasets, we show that the feature-map-transformation approach outperforms state-of-the-art methods for continually-learned GANs, with substantially fewer parameters. The proposed method generates high-quality samples that can also improve the generative-replay-based continual learning for discriminative tasks.

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IJCAI Conference 2021 Conference Paper

Knowledge Consolidation based Class Incremental Online Learning with Limited Data

Mohammed Asad Karim
Vinay Kumar Verma
Pravendra Singh
Vinay Namboodiri
Piyush Rai

We propose a novel approach for class incremental online learning in a limited data setting. This problem setting is challenging because of the following constraints: (1) Classes are given incrementally, which necessitates a class incremental learning approach; (2) Data for each class is given in an online fashion, i. e. , each training example is seen only once during training; (3) Each class has very few training examples; and (4) We do not use or assume access to any replay/memory to store data from previous classes. Therefore, in this setting, we have to handle twofold problems of catastrophic forgetting and overfitting. 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. Our proposed method leverages the meta-learning framework with knowledge consolidation. The meta-learning framework helps the model for rapid learning when samples appear in an online fashion. Simultaneously, knowledge consolidation helps to learn a robust representation against forgetting under online updates to facilitate future learning. Our approach significantly outperforms other methods on several benchmarks.

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NeurIPS Conference 2020 Conference Paper

Calibrating CNNs for Lifelong Learning

Pravendra Singh
Vinay Kumar Verma
Pratik Mazumder
Lawrence Carin
Piyush Rai

We present an approach for lifelong/continual learning of convolutional neural networks (CNN) that does not suffer from the problem of catastrophic forgetting when moving from one task to the other. We show that the activation maps generated by the CNN trained on the old task can be calibrated using very few calibration parameters, to become relevant to the new task. Based on this, we calibrate the activation maps produced by each network layer using spatial and channel-wise calibration modules and train only these calibration parameters for each new task in order to perform lifelong learning. Our calibration modules introduce significantly less computation and parameters as compared to the approaches that dynamically expand the network. Our approach is immune to catastrophic forgetting since we store the task-adaptive calibration parameters, which contain all the task-specific knowledge and is exclusive to each task. Further, our approach does not require storing data samples from the old tasks, which is done by many replay based methods. We perform extensive experiments on multiple benchmark datasets (SVHN, CIFAR, ImageNet, and MS-Celeb), all of which show substantial improvements over state-of-the-art methods (e. g. , a 29% absolute increase in accuracy on CIFAR-100 with 10 classes at a time). On large-scale datasets, our approach yields 23. 8% and 9. 7% absolute increase in accuracy on ImageNet-100 and MS-Celeb-10K datasets, respectively, by employing very few (0. 51% and 0. 35% of model parameters) task-adaptive calibration parameters.

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AAAI Conference 2020 Conference Paper

Meta-Learning for Generalized Zero-Shot Learning

Vinay Kumar Verma
Dhanajit Brahma
Piyush Rai

Learning to classify unseen class samples at test time is popularly referred to as zero-shot learning (ZSL). If test samples can be from training (seen) as well as unseen classes, it is a more challenging problem due to the existence of strong bias towards seen classes. This problem is generally known as generalized zero-shot learning (GZSL). Thanks to the recent advances in generative models such as VAEs and GANs, sample synthesis based approaches have gained considerable attention for solving this problem. These approaches are able to handle the problem of class bias by synthesizing unseen class samples. However, these ZSL/GZSL models suffer due to the following key limitations: (i) Their training stage learns a class-conditioned generator using only seen class data and the training stage does not explicitly learn to generate the unseen class samples; (ii) They do not learn a generic optimal parameter which can easily generalize for both seen and unseen class generation; and (iii) If we only have access to a very few samples per seen class, these models tend to perform poorly. In this paper, we propose a meta-learning based generative model that naturally handles these limitations. The proposed model is based on integrating model-agnostic meta learning with a Wasserstein GAN (WGAN) to handle (i) and (iii), and uses a novel task distribution to handle (ii). Our proposed model yields signiﬁcant improvements on standard ZSL as well as more challenging GZSL setting. In ZSL setting, our model yields 4. 5%, 6. 0%, 9. 8%, and 27. 9% relative improvements over the current state-of-the-art on CUB, AWA1, AWA2, and aPY datasets, respectively.

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IJCAI Conference 2019 Conference Paper

Play and Prune: Adaptive Filter Pruning for Deep Model Compression

Pravendra Singh
Vinay Kumar Verma
Piyush Rai
Vinay P. Namboodiri

While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters. This results in significant memory requirement as well as computational overheads. Consequently, there is a growing need for filter-level pruning approaches for compressing CNN based models that not only reduce the total number of parameters but reduce the overall computation as well. We present a new min-max framework for filter-level pruning of CNNs. 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. Our framework consists of two modules: (1) An adaptive filter pruning (AFP) module, which minimizes the number of filters in the model; and (2) A pruning rate controller (PRC) module, which maximizes the accuracy during pruning. Moreover, unlike most previous approaches, our approach allows directly specifying the desired error tolerance instead of pruning level. Our compressed models can be deployed at run-time, without requiring any special libraries or hardware. Our approach reduces the number of parameters of VGG-16 by an impressive factor of 17. 5X, and number of FLOPS by 6. 43X, with no loss of accuracy, significantly outperforming other state-of-the-art filter pruning methods.

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UAI Conference 2017 Conference Paper

A Probabilistic Framework for Multi-Label Learning with Unseen Labels

Abhilash Gaure
Aishwarya Gupta 0001
Vinay Kumar Verma
Piyush Rai

We present a probabilistic framework for multi-label learning for the setting when the test data may require predicting labels that were not available at training time (i. e. , the zero-shot learning setting). We develop a probabilistic model that leverages the co-occurrence statistics of the labels via a joint generative model for the label matrix (which denotes the label presence/absence for each training example) and for the label cooccurrence matrix (which denotes how many times a pair of labels co-occurs with each other). In addition to handling the unseen labels at test time, leveraging the co-occurrence information may also help in the standard multi-label learning setting, especially if the number of training examples is very small and/or the label matrix of training examples has a large fraction of missing entries. Our experimental results demonstrate the efficacy of our model in handling unseen labels.

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