A Survey of Few-Shot Learning for Image Classification of Aerial Objects

Cai, Haoxin; Zhu, Xuanyue; Wen, Peng; Wang, Han; Le, Wei

doi:10.1007/978-981-19-7652-0_50

Cited by 1 publication

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“…Extensions to explainable classifiers using heterogeneous transfer learning and open-set domain adaptation paradigms can be a possible future avenue to explore. Few Shot Learning [166,167] aims to learn classifiers from fewer examples by leveraging features learned from related classes having a larger number of instances. For instance, a zebra can be considered as an animal with a horse-like body and tiger-like stripes.…”

Section: Future Workmentioning

confidence: 99%

Explainable Image Classification: The Journey So Far and the Road Ahead

Kamakshi,

Krishnan

2023

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Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in the field of XAI, focusing on the tradeoff between model accuracy and interpretability. Motivated by the need to address this tradeoff, we conduct an extensive review of the literature, presenting a multi-view taxonomy that offers a new perspective on XAI methodologies. We analyze various sub-categories of XAI methods, considering their strengths, weaknesses, and practical challenges. Moreover, we explore causal relationships in model explanations and discuss approaches dedicated to explaining cross-domain classifiers. The latter is particularly important in scenarios where training and test data are sampled from different distributions. Drawing insights from our analysis, we propose future research directions, including exploring explainable allied learning paradigms, developing evaluation metrics for both traditionally trained and allied learning-based classifiers, and applying neural architectural search techniques to minimize the accuracy–interpretability tradeoff. This survey paper provides a comprehensive overview of the state-of-the-art in XAI, serving as a valuable resource for researchers and practitioners interested in understanding and advancing the field.

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Section: Future Workmentioning

confidence: 99%

Explainable Image Classification: The Journey So Far and the Road Ahead

Kamakshi,

Krishnan

2023

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A Survey of Few-Shot Learning for Image Classification of Aerial Objects

Cited by 1 publication

References 21 publications

Explainable Image Classification: The Journey So Far and the Road Ahead

Explainable Image Classification: The Journey So Far and the Road Ahead

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