Laplacian Regularized Few-Shot Learning

TL;DR

This paper introduces a transductive Laplacian regularization method for few-shot learning that improves classification accuracy without retraining the base model, using graph clustering principles for efficient inference.

Contribution

It presents a novel Laplacian-regularized inference approach for few-shot learning that is computationally efficient, does not require retraining, and outperforms existing methods across multiple benchmarks.

Findings

Consistently outperforms state-of-the-art methods on five benchmarks.

Achieves high accuracy with fast inference times comparable to inductive methods.

Effective across different models, settings, and datasets.

Abstract

We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment function containing two terms: (1) a unary term assigning query samples to the nearest class prototype, and (2) a pairwise Laplacian term encouraging nearby query samples to have consistent label assignments. Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set. We derive a computationally efficient bound optimizer of a relaxation of our function, which computes independent (parallel) updates for each query sample, while guaranteeing convergence. Following a simple cross-entropy training on the base classes, and without complex meta-learning strategies, we conducted comprehensive experiments over five few-shot learning benchmarks. Our LaplacianShot consistently outperforms state-of-the-art methods by significant margins across different models, settings, and data sets. Furthermore, our transductive inference is very fast, with computational times that are close to inductive inference, and can be used for large-scale few-shot tasks.