Metric Learning
555 papers with code • 8 benchmarks • 32 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
Benchmarks
These leaderboards are used to track progress in Metric Learning
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Libraries
Use these libraries to find Metric Learning models and implementationsDatasets
CUB-200-2011
CASIA-WebFace
Stanford Online Products
In-Shop
VIPeR
Structured3D
ABO
CARS196
VeRi-Wild
WildDeepfake
Most implemented papers
Deep Cosine Metric Learning for Person Re-Identification
nwojke/cosine_metric_learning
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Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities.
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
idstcv/SoftTriple
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ICCV 2019
The set of triplet constraints has to be sampled within the mini-batch.
Cross-Batch Memory for Embedding Learning
MalongTech/research-xbm
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CVPR 2020
This suggests that the features of instances computed at preceding iterations can be used to considerably approximate their features extracted by the current model.
Deep Learning for Person Re-identification: A Survey and Outlook
The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets.
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
tfzhou/ContrastiveSeg
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ICCV 2021
Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting.
A Metric Learning Reality Check
KevinMusgrave/powerful-benchmarker
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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.
Understanding the Role of the Projector in Knowledge Distillation
roymiles/simple-recipe-distillation
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We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance.
Deep metric learning using Triplet network
Deep learning has proven itself as a successful set of models for learning useful semantic representations of data.
Deep Metric Learning via Lifted Structured Feature Embedding
Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning.
