46,848 research outputs found
A Meta-Learning Approach for Custom Model Training
Transfer-learning and meta-learning are two effective methods to apply
knowledge learned from large data sources to new tasks. In few-class, few-shot
target task settings (i.e. when there are only a few classes and training
examples available in the target task), meta-learning approaches that optimize
for future task learning have outperformed the typical transfer approach of
initializing model weights from a pre-trained starting point. But as we
experimentally show, meta-learning algorithms that work well in the few-class
setting do not generalize well in many-shot and many-class cases. In this
paper, we propose a joint training approach that combines both
transfer-learning and meta-learning. Benefiting from the advantages of each,
our method obtains improved generalization performance on unseen target tasks
in both few- and many-class and few- and many-shot scenarios.Comment: AAAI 201
Meta-transfer learning for few-shot learning
10.1109/CVPR.2019.00049CVPR 2019403-41
Few-shot image classification : current status and research trends
Conventional image classification methods usually require a large number of training samples for the training model. However, in practical scenarios, the amount of available sample data is often insufficient, which easily leads to overfitting in network construction. Few-shot learning provides an effective solution to this problem and has been a hot research topic. This paper provides an intensive survey on the state-of-the-art techniques in image classification based on few-shot learning. According to the different deep learning mechanisms, the existing algorithms are di-vided into four categories: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. Transfer learning based methods transfer useful prior knowledge from the source domain to the target domain. Meta-learning based methods employ past prior knowledge to guide the learning of new tasks. Data augmentation based methods expand the amount of sample data with auxiliary information. Multimodal based methods use the information of the auxiliary modal to facilitate the implementation of image classification tasks. This paper also summarizes the few-shot image datasets available in the literature, and experimental results tested by some representative algorithms are provided to compare their performance and analyze their pros and cons. In addition, the application of existing research outcomes on few-shot image classification in different practical fields are discussed. Finally, a few future research directions are iden-tified. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
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