This paper presents a deep relational metric learning (DRML) framework for image
clustering and retrieval. Most existing deep metric learning methods learn an embedding …

Abstract Deep Metric Learning (DML) proposes to learn metric spaces which encode
semantic similarities as embedding space distances. These spaces should be transferable …

In this paper, we propose a deep compositional metric learning (DCML) framework for
effective and generalizable similarity measurement between images. Conventional deep …

This paper presents a novel method for embedding transfer, a task of transferring knowledge
of a learned embedding model to another. Our method exploits pairwise similarities between …

Recent methods for deep metric learning have been focusing on designing different
contrastive loss functions between positive and negative pairs of samples so that the …

Objective functions that optimize deep neural networks play a vital role in creating an
enhanced feature representation of the input data. Although cross-entropy-based loss …

We propose a novel method for anomaly detection primarily aiming at autonomous driving.
The design of the method, called DaCUP (Detection of anomalies as Consistent …

Deep feature embedding aims to learn discriminative features or feature embeddings for
image samples which can minimize their intra-class distance while maximizing their inter …

Since the widespread availability of cameras black-and-white (BW) photography has been a
popular choice for artistic and aesthetic expression. It highlights the main subject in varying …

Deep metric learning is crucial for finding an embedding function that can generalize to
training and testing data, including unknown test classes. However, limited training samples …