# Low-shot learning with large-scale diffusion

## TL;DR

This paper introduces a large-scale label propagation method for low-shot image classification, leveraging similarity graphs over hundreds of millions of images to achieve state-of-the-art accuracy.

## Contribution

It demonstrates that scaling label propagation to massive datasets significantly improves low-shot learning performance.

## Key findings

- Scaling label propagation to hundreds of millions of images yields state-of-the-art accuracy.
- Large-scale similarity graphs effectively support label propagation in low-shot learning.
- The approach outperforms traditional fine-tuning methods in low-data regimes.

## Abstract

This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-shot learning, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes for which training examples are abundant. We consider a semi-supervised setting based on a large collection of images to support label propagation. This is possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling label propagation up to hundred millions of images leads to state of the art accuracy in the low-shot learning regime.