Search Results for author:
Found 3 papers, 2 papers with code
Meta-Tasks: An alternative view on Meta-Learning Regularization
The ability to generalize effectively on both novel and training tasks is a significant barrier to FSL.
Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning
We use augmented samples as the query set during the training phase of the unsupervised meta-learning.
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data
So, many forms of data augmentation techniques are applied to the classifiers to compensate for the lack of training samples.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
