Metric Learning
557 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
Latest papers with no code
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems.
Piecewise-Linear Manifolds for Deep Metric Learning
For this purpose, we propose to model the high-dimensional data manifold using a piecewise-linear approximation, with each low-dimensional linear piece approximating the data manifold in a small neighborhood of a point.
Hyperbolic Metric Learning for Visual Outlier Detection
Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications.
Explore In-Context Segmentation via Latent Diffusion Models
In-context segmentation has drawn more attention with the introduction of vision foundation models.
A Distance Metric Learning Model Based On Variational Information Bottleneck
Compared with the general metric learning model MetricF, the prediction error is reduced by 7. 29%.
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series
Distance-based time series anomaly detection methods are prevalent due to their relative non-parametric nature and interpretability.
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges.
Simple But Effective: Rethinking the Ability of Deep Learning in fNIRS to Exclude Abnormal Input
Our study shows that while current networks in fNIRS are highly accurate for predictions within their training distribution, they falter at identifying and excluding abnormal data which is out-of-distribution, affecting their reliability.
Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification
The primary hurdle in combat identification in remote sensing imagery is the accurate recognition of Novel/Unknown types of aircraft in addition to Known types.
Goal-Conditioned Offline Reinforcement Learning via Metric Learning
Experimentally, we show how our method consistently outperforms other offline RL baselines in learning from sub-optimal offline datasets.
