Metric Learning
558 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
Classification is a Strong Baseline for Deep Metric Learning
azgo14/classification_metric_learning
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Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
Ranked List Loss for Deep Metric Learning
To address this, we propose to build a set-based similarity structure by exploiting all instances in the gallery.
Hardness-Aware Deep Metric Learning
This paper presents a hardness-aware deep metric learning (HDML) framework.
Correlation Congruence for Knowledge Distillation
yoshitomo-matsubara/torchdistill
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ICCV 2019
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.
Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning
MalongTech/research-ms-loss
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CVPR 2019
A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning.
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.
MIC: Mining Interclass Characteristics for Improved Metric Learning
Confusezius/metric-learning-mining-interclass-characteristics
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ICCV 2019
In contrast, we propose to explicitly learn the latent characteristics that are shared by and go across object classes.
R2D2: Reliable and Repeatable Detector and Descriptor
We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
The Group Loss for Deep Metric Learning
dvl-tum/group_loss
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ECCV 2020
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Associative Alignment for Few-shot Image Classification
ML-Bee/associative-alignment-fs
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ECCV 2020
Few-shot image classification aims at training a model from only a few examples for each of the "novel" classes.
