Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Subject: search.filters.subject.Few-shot learning

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    A review on NLP zero-shot and few-shot learning: methods and applications
    (Springer Nature, 2025) Ramesh, G.; Sahil, M.; Palan, S.A.; Bhandary, D.; Ashok, T.A.; J, J.; Sowjanya, N.
    Zero-shot and few-shot learning techniques in natural language processing (NLP), this comprehensive review traces their evolution from traditional methods to cutting-edge approaches like transfer learning and pre-trained language models, semantic embedding, attribute-based approaches, generative models for data augmentation in zero-shot learning, and meta-learning, model-agnostic meta-learning, relationship networks, model-agnostic meta-learning (MAML), prototypical networks in few-shot learning. Real-world applications underscore the adaptability and efficacy of these techniques across various NLP tasks in both industry and academia. Acknowledging challenges inherent in zero-shot and few-shot learning, this review identifies limitations and suggests avenues for improvement. It emphasizes theoretical foundations alongside practical considerations such as accuracy and generalization across diverse NLP tasks. By consolidating key insights, this review provides researchers and practitioners with valuable guidance on the current state and future potential of zero-shot and few-shot learning techniques in addressing real-world NLP challenges. Looking ahead, this review aims to stimulate further research, fostering a deeper understanding of the complexities and applicability of zero-shot and few-shot learning techniques in NLP. By offering a roadmap for future exploration, it seeks to contribute to the ongoing advancement and practical implementation of NLP technologies across various domains. © The Author(s) 2025.
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    Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification
    (Institute of Electrical and Electronics Engineers Inc., 2025) Vincent, A.M.; Padikkal, P.; Bini, A.A.
    This paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification applications often suffer from data inadequacy. Few-shot learning addresses this challenge by enabling learning from limited samples. Meta-learning, a prominent tool for few-shot learning, learns across multiple classification tasks. We explore different types of meta-learners, with a particular focus on metric-based models. We analyze the potential of hyperparameter optimization techniques, specifically Bayesian optimization and its variants, to enhance the performance of these models. Experimental results on the Omniglot and ImageNet datasets demonstrate that incorporating Bayesian optimization, particularly its evolutionary strategy variant, into meta-learning frameworks leads to improved accuracy compared to settings without hyperparameter optimization. Here, we show that by optimizing hyperparameters for individual tasks rather than using a uniform setting, we achieve notable gains in model performance, underscoring the importance of tailored hyperparameter configurations in meta-learning. © 2013 IEEE.