Few-shot Learning

What is Few-shot Learning?

Few-shot learning is a machine learning paradigm that aims to train models to recognize new classes with only a small number of labeled examples. This is in contrast to traditional machine learning, which typically requires large amounts of labeled data to achieve good performance. Few-shot learning is particularly relevant in situations where obtaining labeled data is expensive, time-consuming, or otherwise challenging.

How does Few-shot Learning work?

Few-shot learning techniques often leverage prior knowledge learned from related tasks or use meta-learning approaches in which the model learns to learn from few examples. Some common few-shot learning techniques include memory-augmented neural networks, metric learning, and gradient-based meta-learning methods, such as MAML.

Resources for Few-shot Learning: