Self-Supervised Learning For Few-Shot Image Classification

Understand

Few-shot image classification aims to classify unseen classes with limited labelled samples.

• Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing.
• Due to the limited number of samples for each task, the initial embedding network for meta-learning becomes an essential component and can largely affect the performance in practice.
• To this end, most of the existing methods highly rely on the efficient embedding network.