Search Results for author:
Found 4 papers, 2 papers with code
Model-Agnostic Learning to Meta-Learn
In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution.
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.
Revisiting Few-Shot Learning for Facial Expression Recognition
Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest impact on performance.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Regression Networks for Meta-Learning Few-Shot Classification
We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class.
