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<p>Recent advancements in Deep Neural Networks (DNN) have shown impressive performance in domains like image classification, machine translation, and natural language processing, particularly with abundant training data. DNNs face challenges with limited datasets, leading to overfitting and suboptimal generalization, especially in real-world scenarios with data scarcity. Few-Shot Learning (FSL) addresses the challenges by leveraging prior knowledge to enhance machine vision systems with limited training samples, enabling rapid generalization. The paper aims to address four key research questions concerning FSL, focusing on its impact on image analysis, the significance of existing FSL techniques, how FSL enhances performance in image analysis, and recommendations for the best FSL techniques across different domains. According to the literature review, the FSL approaches are categorized into five major categories: transfer learning, metric learning, data augmentation methods, meta-learning, and the Bayesian approach, highlighting the advantages and drawbacks of each technique. The paper also presents a brief overview of well-known datasets and prominent publications. A reference for choosing an appropriate FSL method is provided, utilizing a comparative examination of various techniques for image classification assignments. This paper serves as a concise resource to promptly understand the basics, benefits, and hurdles of diverse FSL methods applied to image analysis tasks, focusing on future research directions. It can be concluded from the study's findings that meta-learning, along with the attention approach and hybrid approach, can be utilized to enhance the overall efficiency of image classification tasks.</p>

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A review on the effectiveness of recent approaches to few-shot learning for image analysis

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Deepak Suresh Asudani,
  • Naresh Kumar Nagwani,
  • Pradeep Singh

摘要

Recent advancements in Deep Neural Networks (DNN) have shown impressive performance in domains like image classification, machine translation, and natural language processing, particularly with abundant training data. DNNs face challenges with limited datasets, leading to overfitting and suboptimal generalization, especially in real-world scenarios with data scarcity. Few-Shot Learning (FSL) addresses the challenges by leveraging prior knowledge to enhance machine vision systems with limited training samples, enabling rapid generalization. The paper aims to address four key research questions concerning FSL, focusing on its impact on image analysis, the significance of existing FSL techniques, how FSL enhances performance in image analysis, and recommendations for the best FSL techniques across different domains. According to the literature review, the FSL approaches are categorized into five major categories: transfer learning, metric learning, data augmentation methods, meta-learning, and the Bayesian approach, highlighting the advantages and drawbacks of each technique. The paper also presents a brief overview of well-known datasets and prominent publications. A reference for choosing an appropriate FSL method is provided, utilizing a comparative examination of various techniques for image classification assignments. This paper serves as a concise resource to promptly understand the basics, benefits, and hurdles of diverse FSL methods applied to image analysis tasks, focusing on future research directions. It can be concluded from the study's findings that meta-learning, along with the attention approach and hybrid approach, can be utilized to enhance the overall efficiency of image classification tasks.