Deep Triplet Ranking Networks for One-Shot Recognition

TL;DR

This paper introduces a deep triplet ranking network for one-shot learning, enabling classification with only one example per class by learning universal embeddings and incorporating new class instances.

Contribution

It proposes an end-to-end deep triplet ranking model that learns class-agnostic embeddings and integrates one-shot instances for improved recognition performance.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Effectively incorporates new class instances into the embedding space.

Demonstrates robustness in one-shot recognition scenarios.

Abstract

Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class. To tackle this natural and important challenge, one-shot learning, which aims to exploit a set of well labeled base classes to build classifiers for the new target classes that have only one observed instance per class, has recently received increasing attention from the research community. In this paper we propose a novel end-to-end deep triplet ranking network to perform one-shot learning. The proposed approach learns class universal image embeddings on the well labeled base classes under a triplet ranking loss, such that the instances from new classes can be categorized based on their similarity with the one-shot instances in the learned embedding space. Moreover, our approach can naturally incorporate the available one-shot instances from the new classes into the embedding learning process to improve the triplet ranking model. We conduct experiments on two popular datasets for one-shot learning. The results show the proposed approach achieves better performance than the state-of-the- art comparison methods.