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 2026–2050 of 6661 papers

TitleStatusHype
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—0
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations—0
CO2: Consistent Contrast for Unsupervised Visual Representation Learning—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
FedRGL: Robust Federated Graph Learning for Label Noise—0
CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
CMV-BERT: Contrastive multi-vocab pretraining of BERT—0
CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings—0
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series—0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
Combined Scaling for Zero-shot Transfer Learning—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis—0
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection—0
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement—0
Disentangle Perceptual Learning through Online Contrastive 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