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Metalearning has been widely applied for implementing few-shot learning and fast model adaptation. Particularly, existing metalearning methods have been exploited to learn the control mechanism for gradient descent processes, in an effort to facilitate gradient-based learning in gaining high speed and generalization ability. This article presents a novel method that controls the gradient descent process of the model parameters in a neural network, by limiting the model parameters within a low-dimensional latent space. The main challenge for implementing this idea is that a decoder with many parameters may be required. To tackle this problem, the article provides an alternative design of the decoder with a structure that shares certain weights, thereby reducing the number of required parameters. In addition, this work combines ensemble learning with the proposed approach to improve the overall learning performance. Systematic experimental studies demonstrate that the proposed approach offers results superior to the state of the art in performing the Omniglot classification and miniImageNet classification tasks.
|Number of pages||14|
|Journal||IEEE Transactions on Cybernetics|
|Early online date||01 Dec 2021|
|Publication status||Published - 01 Jun 2023|
- ensemble learning
- latent variable
- Mathematical models
- Task analysis
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- 1 Finished
Ser Cymru: Reconstruction of Missing Information in Optical Remote Sensing Images Based on Deep Learning and Knowledge Interpolation
01 Oct 2020 → 28 Feb 2023
Project: Externally funded research