Gene

Context (archaeology)

Biology

Statistics

ENCODE

Contextual image classification

Classifier (UML)

Economics

Biochemistry

Computer science

Mathematical optimization

Paleontology

Task (project management)

Pattern recognition (psychology)

Mathematics

Management

Image (mathematics)

Expectation–maximization algorithm

Artificial intelligence

Chemistry

Mutual information

Maximum likelihood

Maximization

Machine learning

Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning methods for few shot image classification due to its simplicity and effectiveness. In this work, we present Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which introduces two novel contributions: i) Mutual information maximization between generated weights and data within the task; this enables the generated weights to retain information of the task and the specific query sample. ii) Self-attention and cross-attention paths to encode the context of the task and individual queries. Both two contributions are shown to be very effective in extensive experiments. Overall, AWGIM is competitive with state-of-the-art. Code is available at https://github.com/Yiluan/AWGIM.

Attentive Weights Generation for Few Shot Learning via Information Maximization