Breaking standard machine learning paradigms: few-shot learning for computer vision

About this talk

This talk will explore the concept of few-shot learning and its applications in computer vision.

Few-shot learning is a machine learning paradigm that allows a model to learn and generalize from a small number of examples. The talk will cover how this approach differs from traditional machine learning methods, which typically require large amounts of data.

Isha will discuss various techniques for implementing few-shot learning in computer vision, such as meta-learning, and demonstrate how these techniques can improve the performance of models on tasks such as image classification and object detection.