SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 5001–5025 of 6661 papers

TitleStatusHype
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning—0
Graph Contrastive Learning for Multi-omics Data—0
A contrastive learning approach for individual re-identification in a wild fish population—0
Learning Invariance from Generated Variance for Unsupervised Person Re-identificationCode0
SIRL: Similarity-based Implicit Representation Learning—0
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action RecognitionCode0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
ToThePoint: Efficient Contrastive Learning of 3D Point Clouds via RecyclingCode0
Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification—0
Difficulty-Based Sampling for Debiased Contrastive Representation Learning—0
WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos—0
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning—0
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation—0
LAC - Latent Action Composition for Skeleton-based Action Segmentation—0
Enhanced Soft Label for Semi-Supervised Semantic Segmentation—0
Learning Transformation-Predictive Representations for Detection and Description of Local Features—0
CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised LearningCode0
Promoting Semantic Connectivity: Dual Nearest Neighbors Contrastive Learning for Unsupervised Domain Generalization—0
Semantic Information in Contrastive LearningCode0
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval—0
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement—0
EC2: Emergent Communication for Embodied Control—0
Scene Graph Contrastive Learning for Embodied Navigation—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified