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
Libraries
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Latest papers with no code
FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning
However, the dominance of center loss over the other losses leads to the model missing features sensitive to them.
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes.
DeepFunction: Deep Metric Learning-based Imbalanced Classification for Diagnosing Threaded Pipe Connection Defects using Functional Data
To classify the defect samples based on imbalanced, multichannel, and incomplete functional data is very important but challenging.
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.