.png){kind=icon}
Return
Decoder Choice Network for Metalearning
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2021.3123403.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Task analysis
Decoding
Training
Testing
Measurement
Optimization
Mathematical models
Decoder
ensemble learning
latent variable
metalearning
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSaveShareSaveShareSave View more