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 6626–6650 of 6661 papers

TitleStatusHype
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data—0
TabGSL: Graph Structure Learning for Tabular Data Prediction—0
Table-based Fact Verification with Self-labeled Keypoint Alignment—0
Tac2Pose: Tactile Object Pose Estimation from the First Touch—0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
Tackling Online One-Class Incremental Learning by Removing Negative Contrasts—0
Deeper-GXX: Deepening Arbitrary GNNs—0
CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning—0
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning—0
TaCo: Textual Attribute Recognition via Contrastive Learning—0
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment—0
TacticExpert: Spatial-Temporal Graph Language Model for Basketball Tactics—0
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering—0
TaDSE: Template-aware Dialogue Sentence Embeddings—0
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification—0
TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension—0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning—0
Task-Induced Representation Learning—0
Taxonomy Inference for Tabular Data Using Large Language Models—0
Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data—0
TCBERT: A Technical Report for Chinese Topic Classification BERT—0
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—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