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

Showing 22512275 of 6661 papers

TitleStatusHype
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic ImagesCode0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
Mask-Guided Contrastive Attention Model for Person Re-IdentificationCode0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Mask-informed Deep Contrastive Incomplete Multi-view ClusteringCode0
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-IdentificationCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
Masked Student Dataset of ExpressionsCode0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained AlignmentCode0
Masked Collaborative Contrast for Weakly Supervised Semantic SegmentationCode0
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive LearningCode0
Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment LocalizationCode0
Adversarial Modality Alignment Network for Cross-Modal Molecule RetrievalCode0
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation AlignmentCode0
ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography ClassificationCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
Manifold Contrastive Learning with Variational Lie Group OperatorsCode0
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text ClassificationCode0
Making the Most of Text Semantics to Improve Biomedical Vision--Language ProcessingCode0
MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive LearningCode0
CCFC: Bridging Federated Clustering and Contrastive LearningCode0
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge GraphsCode0
Correlation between Alignment-Uniformity and Performance of Dense Contrastive RepresentationsCode0
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERUCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified