Few-Shot Learning with Per-Sample Rich Supervision

TL;DR

This paper introduces a novel few-shot learning method that leverages per-sample semantic relevance information, reducing sample complexity and improving generalization, especially in online and non-stationary data scenarios.

Contribution

It proposes a new approach using per-sample feature relevance, an ellipsoid-margin loss, and an online algorithm, advancing few-shot learning beyond traditional transfer and meta-learning methods.

Findings

Improved generalization error bounds for the proposed method.

Enhanced performance on scene and bird classification benchmarks.

Effective in online and non-stationary data stream settings.

Abstract

Learning with few samples is a major challenge for parameter-rich models like deep networks. In contrast, people learn complex new concepts even from very few examples, suggesting that the sample complexity of learning can often be reduced. Many approaches to few-shot learning build on transferring a representation from well-sampled classes, or using meta learning to favor architectures that can learn with few samples. Unfortunately, such approaches often struggle when learning in an online way or with non-stationary data streams. Here we describe a new approach to learn with fewer samples, by using additional information that is provided per sample. Specifically, we show how the sample complexity can be reduced by providing semantic information about the relevance of features per sample, like information about the presence of objects in a scene or confidence of detecting attributes in an image. We provide an improved generalization error bound for this case. We cast the problem of using per-sample feature relevance by using a new ellipsoid-margin loss, and develop an online algorithm that minimizes this loss effectively. Empirical evaluation on two machine vision benchmarks for scene classification and fine-grain bird classification demonstrate the benefits of this approach for few-shot learning.