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

Showing 401450 of 1648 papers

TitleStatusHype
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for ImagesCode0
In Defense of the Triplet Loss for Person Re-IdentificationCode0
Inspecting class hierarchies in classification-based metric learning modelsCode0
Merging datasets through deep learningCode0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Label a Herd in Minutes: Individual Holstein-Friesian Cattle IdentificationCode0
Learning Neural Models for End-to-End ClusteringCode0
Improving Collaborative Metric Learning with Efficient Negative SamplingCode0
Improved Embeddings with Easy Positive Triplet MiningCode0
Deep Metric Learning Beyond Binary SupervisionCode0
Disambiguating Music Artists at Scale with Audio Metric LearningCode0
Metric Learning for Adversarial RobustnessCode0
Metric Learning for Dynamic Text ClassificationCode0
Discrete Scale-invariant Metric Learning for Efficient Collaborative FilteringCode0
Improving Generalization of Metric Learning via Listwise Self-distillationCode0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
Adaptive Graph Convolutional Neural NetworksCode0
IDEAL: Independent Domain Embedding Augmentation LearningCode0
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating TheoremCode0
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image ClassificationCode0
Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine SimilarityCode0
Human Motion Analysis with Deep Metric LearningCode0
Batch DropBlock Network for Person Re-identification and BeyondCode0
Deep Meta Metric LearningCode0
Disentangling by Subspace DiffusionCode0
Dissecting Deep Metric Learning Losses for Image-Text RetrievalCode0
Batch Decorrelation for Active Metric LearningCode0
How Shift Equivariance Impacts Metric Learning for Instance SegmentationCode0
Multi-Level Correlation Network For Few-Shot Image ClassificationCode0
HSE: Hybrid Species Embedding for Deep Metric LearningCode0
Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric LearningCode0
Deep Hashing via Householder QuantizationCode0
Heated-Up Softmax EmbeddingCode0
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain NetworksCode0
A Lower Bound of Hash Codes' PerformanceCode0
Distance-Ratio-Based Formulation for Metric LearningCode0
Hierarchical Latent Relation Modeling for Collaborative Metric LearningCode0
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity LearningCode0
Hard-Aware Point-to-Set Deep Metric for Person Re-identificationCode0
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot FillingCode0
Hard-Aware Deeply Cascaded EmbeddingCode0
Hardness-Aware Deep Metric LearningCode0
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric LearningCode0
Ground Metric Learning on GraphsCode0
RelationNet2: Deep Comparison Columns for Few-Shot LearningCode0
DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric SpaceCode0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Divide and Conquer the Embedding Space for Metric LearningCode0
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep LearningCode0
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@198.2Unverified
2Hyp-DINO 8x8R@192.8Unverified
3ResNet-50 + AVSLR@191.5Unverified
4NEDR@191.5Unverified
5ResNet-50 + Intra-Batch (ensemble of 5)R@191.5Unverified
6EfficientDML-VPTSP-G/512R@191.2Unverified
7CCL (ResNet-50)R@191.02Unverified
8ResNet50 + LanguageR@190.2Unverified
9ResNet-50 + MetrixR@189.6Unverified
10ResNet50 + S2SDR@189.5Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@191.2Unverified
2STIRR@188.3Unverified
3Recall@k Surrogate Loss (ViT-B/16)R@188Unverified
4ViT-TripletR@186.5Unverified
5ROADMAP (DeiT-S)R@186Unverified
6Hyp-ViTR@185.9Unverified
7Hyp-DINOR@185.1Unverified
8Recall@k Surrogate Loss (ViT-B/32)R@185.1Unverified
9CCL (ResNet-50)R@183.1Unverified
10ROADMAP (ResNet-50)R@183.1Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@190.1Unverified
2EfficientDML-VPTSP-G/512R@188.5Unverified
3Hyp-ViTR@185.6Unverified
4Hyp-DINOR@180.9Unverified
5NEDR@174.9Unverified
6CCL (ResNet-50)R@173.45Unverified
7ResNet-50 + AVSLR@171.9Unverified
8ResNet-50 + Intra-Batch ConnectionsR@171.8Unverified
9ResNet50 + LanguageR@171.4Unverified
10ResNet-50 + MetrixR@171.4Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@196.7Unverified
2STIRR@195Unverified
3MGAR@194.3Unverified
4Hyp-ViTR@192.5Unverified
5Hyp-DINOR@192.4Unverified
6CCL (ResNet-50)R@192.31Unverified
7Gradient SurgeryR@192.21Unverified
8ResNet-50 + MetrixR@192.2Unverified
9EfficientDML-VPTSP-G/512R@192.1Unverified
10ViT-TripletR@192.1Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP43.8Unverified
2CSLAverage-mAP31Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP38Unverified
2CSLAverage-mAP28.7Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP37Unverified
2CSLAverage-mAP12.1Unverified