Learning to learn via Self-Critique Learning to learn via Self-Critique
Paper summary ### Key points - Instead of just focusing on supervised learning, a self-critique and adapt network provides a unsupervised learning approach in improving the overall generalization. It does this via transductive learning by learning a label-free loss function from the validation set to improve the base model. - The SCA framework helps a learning algorithm be more robust by learning more relevant features and improve during the training phase. ### Ideas 1. Combine deep learning models with SCA that help improve genearlization when we data is fed into these large networks. 2. Build a SCA that focuses not on learning a label-free loss function but on learning quality of a concept. ### Review Overall, the paper present a novel idea that offers a unsupervised learning method to assist a supervised learning model to improve its performance. Implementation of this SCA framework is straightforward and demonstrates promising results. This approach is finally contributing to the actual theory of meta-learning and learning to learn research field. SCA framework is a new step towards self-adaptive learning systems. Unfortunately, the experimentation is insufficient and provided little insight into how this framework can help in cases where task domains vary in distribution or in concept.
Learning to learn via Self-Critique
Antoniou, Antreas and Storkey, Amos J.
arXiv e-Print archive - 2019 via Local Bibsonomy
Keywords: dblp

Summary by Mikhail Meskhi 5 months ago
Your comment:

ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!

Sponsored by: and