SOTAVerified

Metric Learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 1648 papers

TitleStatusHype
Sparse Metric Learning via Smooth Optimization—0
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels—0
Sparse online relative similarity learning—0
Spatial Assembly Networks for Image Representation Learning—0
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification—0
Spatially Optimized Compact Deep Metric Learning Model for Similarity Search—0
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Spherical Feature Transform for Deep Metric Learning—0
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning—0
Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning—0
Statistical Latent Space Approach for Mixed Data Modelling and Applications—0
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge—0
Stochastic Class-Based Hard Example Mining for Deep Metric Learning—0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples—0
Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone—0
Stochastic Flows and Geometric Optimization on the Orthogonal Group—0
Structural Deep Metric Learning for Room Layout Estimation—0
Structured learning of metric ensembles with application to person re-identification—0
Structure and Semantics Preserving Document Representations—0
Sub-GMN: The Neural Subgraph Matching Network Model—0
Subspace Representation Learning for Few-shot Image Classification—0
Supervised Bipartite Graph Inference—0
Supervised Categorical Metric Learning with Schatten p-Norms—0
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices—0
Supervised Metric Learning for Music Structure Features—0
Supervised Metric Learning with Generalization Guarantees—0
Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning—0
Supervised similarity learning for corporate bonds using Random Forest proximities—0
Supervised Tree-Wasserstein Distance—0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification—0
Synthetic speech detection using meta-learning with prototypical loss—0
Synthetic Speech Source Tracing using Metric Learning—0
Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches—0
Targeted Attention for Generalized- and Zero-Shot Learning—0
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding—0
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification—0
Template Adaptation for Face Verification and Identification—0
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting—0
Droid: A Resource Suite for AI-Generated Code Detection—0
The Classification Accuracy of Multiple-Metric Learning Algorithm on Multi-Sensor Fusion—0
The Expected Jacobian Outerproduct: Theory and Empirics—0
The General Pair-based Weighting Loss for Deep Metric Learning—0
The Group Loss++: A deeper look into group loss for deep metric learning—0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm—0
Three Things to Know about Deep Metric Learning—0
Threshold Auto-Tuning Metric Learning—0
Time-Frequency Analysis of Variable-Length WiFi CSI Signals for Person Re-Identification—0
TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling—0
Towards Adversarially Robust Deep Metric Learning—0
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All datasetsCARS196Stanford Online ProductsCUB-200-2011In-ShopDyML-AnimalDyML-ProductDyML-Vehicle

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@198.2—Unverified
2Hyp-DINO 8x8R@192.8—Unverified
3ResNet-50 + AVSLR@191.5—Unverified
4NEDR@191.5—Unverified
5ResNet-50 + Intra-Batch (ensemble of 5)R@191.5—Unverified
6EfficientDML-VPTSP-G/512R@191.2—Unverified
7CCL (ResNet-50)R@191.02—Unverified
8ResNet50 + LanguageR@190.2—Unverified
9ResNet-50 + MetrixR@189.6—Unverified
10ResNet50 + S2SDR@189.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@191.2—Unverified
2STIRR@188.3—Unverified
3Recall@k Surrogate Loss (ViT-B/16)R@188—Unverified
4ViT-TripletR@186.5—Unverified
5ROADMAP (DeiT-S)R@186—Unverified
6Hyp-ViTR@185.9—Unverified
7Hyp-DINOR@185.1—Unverified
8Recall@k Surrogate Loss (ViT-B/32)R@185.1—Unverified
9CCL (ResNet-50)R@183.1—Unverified
10ROADMAP (ResNet-50)R@183.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@190.1—Unverified
2EfficientDML-VPTSP-G/512R@188.5—Unverified
3Hyp-ViTR@185.6—Unverified
4Hyp-DINOR@180.9—Unverified
5NEDR@174.9—Unverified
6CCL (ResNet-50)R@173.45—Unverified
7ResNet-50 + AVSLR@171.9—Unverified
8ResNet-50 + Intra-Batch ConnectionsR@171.8—Unverified
9ResNet50 + LanguageR@171.4—Unverified
10ResNet-50 + MetrixR@171.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@196.7—Unverified
2STIRR@195—Unverified
3MGAR@194.3—Unverified
4Hyp-ViTR@192.5—Unverified
5Hyp-DINOR@192.4—Unverified
6CCL (ResNet-50)R@192.31—Unverified
7Gradient SurgeryR@192.21—Unverified
8ResNet-50 + MetrixR@192.2—Unverified
9EfficientDML-VPTSP-G/512R@192.1—Unverified
10ViT-TripletR@192.1—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP43.8—Unverified
2CSLAverage-mAP31—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP38—Unverified
2CSLAverage-mAP28.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP37—Unverified
2CSLAverage-mAP12.1—Unverified