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
Latest papers with no code
Metric Learning for 3D Point Clouds Using Optimal Transport
Learning embeddings of any data largely depends on the ability of the target space to capture semantic rela- tions.
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation
Using metric learning through a Siamese Network architecture, we achieve 57. 71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work.
GCC: Generative Calibration Clustering
Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space.
Single-image driven 3d viewpoint training data augmentation for effective wine label recognition
Confronting the critical challenge of insufficient training data in the field of complex image recognition, this paper introduces a novel 3D viewpoint augmentation technique specifically tailored for wine label recognition.
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning
In the training process, we train and get the best entity-span detection model and the entity classification model separately on the source domain using meta-learning, where we create a contrastive learning module to enhance entity representations for entity classification.
Spatially Optimized Compact Deep Metric Learning Model for Similarity Search
Similarity search is a crucial task where spatial features decide an important output.
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.
MPOFI: Multichannel Partially Observed Functional Modeling for Defect Classification with Imbalanced Dataset via Deep Metric Learning
Motivated by a real example from the pipe tightening process, we target at detect classification when each sample is a multichannel functional data.
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.
