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Metric Learning

222 papers with code · Methodology

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

Greatest papers with code

Time-Contrastive Networks: Self-Supervised Learning from Video

23 Apr 2017tensorflow/models

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 VIDEO ALIGNMENT

Disentangling by Subspace Diffusion

NeurIPS 2020 deepmind/deepmind-research

We show that fully unsupervised factorization of a data manifold is possible *if* the true metric of the manifold is known and each factor manifold has nontrivial holonomy -- for example, rotation in 3D.

METRIC LEARNING REPRESENTATION LEARNING

PyTorch Metric Learning

20 Aug 2020KevinMusgrave/pytorch-metric-learning

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

ECCV 2020 KevinMusgrave/pytorch-metric-learning

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

metric-learn: Metric Learning Algorithms in Python

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

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

METRIC LEARNING MODEL SELECTION

In Defense of the Triplet Loss for Person Re-Identification

22 Mar 2017VisualComputingInstitute/triplet-reid

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.

METRIC LEARNING PERSON RE-IDENTIFICATION

Matching Networks for One Shot Learning

NeurIPS 2016 oscarknagg/few-shot

Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.

FEW-SHOT IMAGE CLASSIFICATION LANGUAGE MODELLING METRIC LEARNING OMNIGLOT ONE-SHOT LEARNING

Supervized Segmentation with Graph-Structured Deep Metric Learning

10 May 2019loicland/superpoint_graph

We introduce the graph-structured contrastive loss, a loss function structured by a ground truth segmentation.

METRIC LEARNING

Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

CVPR 2015 zhunzhong07/person-re-ranking

In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA).

METRIC LEARNING PERSON RE-IDENTIFICATION