Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly

TL;DR

This paper introduces BIER, a method that enhances deep metric learning by creating independent embedding ensembles through online gradient boosting, leading to improved image retrieval accuracy without extra test-time parameters.

Contribution

The paper proposes a novel ensemble-based approach with loss functions to increase embedding diversity and robustness in deep metric learning.

Findings

Improves retrieval accuracy on multiple datasets.

Reduces correlation among embeddings effectively.

Compatible with any differentiable loss function.

Abstract

Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of embeddings. In this work, we show how to improve the robustness of such embeddings by exploiting the independence within ensembles. To this end, we divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each learner receives a reweighted training sample from the previous learners. Further, we propose two loss functions which increase the diversity in our ensemble. These loss functions can be applied either for weight initialization or during training. Together, our contributions leverage large embedding sizes more effectively by significantly reducing correlation of the embedding and consequently increase retrieval accuracy of the embedding. Our method works with any differentiable loss function and does not introduce any additional parameters during test time. We evaluate our metric learning method on image retrieval tasks and show that it improves over state-of-the-art methods on the CUB 200-2011, Cars-196, Stanford Online Products, In-Shop Clothes Retrieval and VehicleID datasets.