Augmented domain agreement for adaptable Meta-Learner on Few-Shot classification

Widhianingsih, Tintrim Dwi Ary; Kang, Dae-Ki

doi:10.1007/s10489-021-02744-1

Appl Intell

2021

DOI: 10.1007/s10489-021-02744-1

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Augmented domain agreement for adaptable Meta-Learner on Few-Shot classification

Tintrim Dwi Ary Widhianingsih

Dae-Ki Kang

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Cited by 4 publications

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Match them up: visually explainable few-shot image classification

Wang

Verma

et al. 2022

Appl Intell

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Few-shot learning (FSL) approaches, mostly neural network-based, assume that pre-trained knowledge can be obtained from base (seen) classes and transferred to novel (unseen) classes. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper FSL application in some risk-sensitive areas. In this paper, we reveal a new way to perform FSL for image classification, using a visual representation from the backbone model and patterns generated by a self-attention based explainable module. The representation weighted by patterns only includes a minimum number of distinguishable features and the visualized patterns can serve as an informative hint on the transferred knowledge. On three mainstream datasets, experimental results prove that the proposed method can enable satisfying explainability and achieve high classification results. Code is available at https://github.com/wbw520/MTUNet.

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Match them up: visually explainable few-shot image classification

Wang

Verma

et al. 2022

Appl Intell

View full text Add to dashboard Cite

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Dual-model Collaborative Learning with Knowledge Clustering for Few-shot Image Classification

Xiong,

Cao,

Zhao

2023

Multimed Tools Appl

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Few-shot and meta-learning methods for image understanding: a survey

Lao

et al. 2023

Int J Multimed Info Retr

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State-of-the-art deep learning systems (e.g., ImageNet image classification) typically require very large training sets to achieve high accuracies. Therefore, one of the grand challenges is called few-shot learning where only a few training samples are required for good performance. In this survey, we illuminate one of the key paradigms in few-shot learning called meta-learning. These meta-learning methods, by simulating the tasks which will be presented at inference through episodic training, can effectively employ previous prior knowledge to guide the learning of new tasks. In this paper, we provide a comprehensive overview and key insights into the meta-learning approaches and categorize them into three branches according to their technical characteristics, namely metric-based, model-based and optimization-based meta-learning. Due to the major importance of the evaluation process, we also present an overview of current widely used benchmarks, as well as performances of recent meta-learning methods on these datasets. Based on over 200 papers in this survey, we conclude with the major challenges and future directions of few-shot learning and meta-learning.

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Augmented domain agreement for adaptable Meta-Learner on Few-Shot classification

Cited by 4 publications

References 21 publications

Match them up: visually explainable few-shot image classification

Match them up: visually explainable few-shot image classification

Dual-model Collaborative Learning with Knowledge Clustering for Few-shot Image Classification

Few-shot and meta-learning methods for image understanding: a survey

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