Few-Shot Learning

Privacy Enhancement for Cloud-Based Few-Shot Learning

Accepted in IEEE WCCI International Joint Conference on Neural Networks (IJCNN), July 2022, Padua, Italy

Learning from Few Examples: A Summary of Approaches to Few-Shot Learning

Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensively high computation …

Few-Shot Keyword Spotting With Prototypical Networks

Published in ACM 7th International Conference on Machine Learning Technologies (ICMLT), March 2022, Rome, Italy

Laplacian Regularized Few-Shot Learning

Paper https://arxiv.org/abs/2006.15486 Code https://github.com/imtiazziko/LaplacianShot Main Idea Transfer Learning: Image embeddings are obtained by pre-training a network on the set of base classes using cross-entropy loss. Transductive Inference: Jointly classify all the query examples together.