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 2001–2025 of 6661 papers

TitleStatusHype
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers—0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images—0
Distributed Contrastive Learning for Medical Image Segmentation—0
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation—0
Distortion-Disentangled Contrastive Learning—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
Federated Contrastive Learning for Decentralized Unlabeled Medical Images—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
A two-steps approach to improve the performance of Android malware detectors—0
A Unified and Efficient Contrastive Learning Framework for Text Summarization—0
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes—0
AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify—0
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing—0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning—0
Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation—0
Distilling Localization for Self-Supervised Representation Learning—0
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection—0
CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization—0
Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection—0
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—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