SLADE: A Self-Training Framework For Distance Metric Learning

TL;DR

SLADE introduces a self-training framework that leverages unlabeled data with pseudo labels and feature basis learning to enhance distance metric learning for image retrieval tasks.

Contribution

The paper proposes a novel self-training approach with basis learning to improve metric learning using unlabeled data, outperforming existing methods.

Findings

Significant performance improvements on CUB-200, Cars-196, and In-shop benchmarks.

Effective handling of noisy pseudo labels through basis function learning.

Enhanced retrieval accuracy compared to state-of-the-art methods.

Abstract

Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars-196 and In-shop. Experimental results demonstrate that our approach significantly improves the performance over the state-of-the-art methods.