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 1976–2000 of 6661 papers

TitleStatusHype
OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open DataCode0
MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation—0
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories—0
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning—0
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG—0
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition—0
AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature PromptingCode0
CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous QueriesCode0
M3ANet: Multi-scale and Multi-Modal Alignment Network for Brain-Assisted Target Speaker ExtractionCode0
JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence ClusteringCode0
Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic SegmentationCode0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation—0
MGS3: A Multi-Granularity Self-Supervised Code Search Framework—0
A Mathematical Perspective On Contrastive Learning—0
Efficient Text Encoders for Labor Market Analysis—0
Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone—0
EmotionRankCLAP: Bridging Natural Language Speaking Styles and Ordinal Speech Emotion via Rank-N-Contrast—0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis—0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
Query Routing for Retrieval-Augmented Language Models—0
Skin Lesion Phenotyping via Nested Multi-modal 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