Metric Learning

305 papers with code • 4 benchmarks • 26 datasets

The goal of Metric Learning is to learn a representation function that maps objects into an embedded space. The distance in the embedded space should preserve the objects’ similarity — similar objects get close and dissimilar objects get far away. Various loss functions have been developed for Metric Learning. For example, the contrastive loss guides the objects from the same class to be mapped to the same point and those from different classes to be mapped to different points whose distances are larger than a margin. Triplet loss is also popular, which requires the distance between the anchor sample and the positive sample to be smaller than the distance between the anchor sample and the negative sample.

Source: Road Network Metric Learning for Estimated Time of Arrival

Greatest papers with code

Time-Contrastive Networks: Self-Supervised Learning from Video

tensorflow/models 23 Apr 2017

While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.

Metric Learning Self-Supervised Learning +1

Disentangling by Subspace Diffusion

deepmind/deepmind-research NeurIPS 2020

We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER).

Metric Learning Representation Learning

PyTorch Metric Learning

KevinMusgrave/pytorch-metric-learning 20 Aug 2020

Deep metric learning algorithms have a wide variety of applications, but implementing these algorithms can be tedious and time consuming.

Metric Learning

A Metric Learning Reality Check

KevinMusgrave/pytorch-metric-learning ECCV 2020

Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods.

Metric Learning

Circle Loss: A Unified Perspective of Pair Similarity Optimization

layumi/Person_reID_baseline_pytorch CVPR 2020

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

Face Recognition Image Retrieval +2

In Defense of the Triplet Loss for Person Re-Identification

adambielski/siamese-triplet 22 Mar 2017

In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.

Ranked #3 on Person Re-Identification on CUHK03 (Rank-5 metric)

General Classification Metric Learning +1

Improved Deep Metric Learning with Multi-class N-pair Loss Objective

PaddlePaddle/PaddleClas NeurIPS 2016

Deep metric learning has gained much popularity in recent years, following the success of deep learning.

Face Verification Image Clustering +2

Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval

PaddlePaddle/PaddleClas 25 Dec 2012

Learning Mahanalobis distance metrics in a high- dimensional feature space is very difficult especially when structural sparsity and low rank are enforced to improve com- putational efficiency in testing phase.

Face Verification Metric Learning

metric-learn: Metric Learning Algorithms in Python

scikit-learn-contrib/metric-learn 13 Aug 2019

metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms.

Metric Learning Model Selection