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 4501–4550 of 6661 papers

TitleStatusHype
RepsNet: Combining Vision with Language for Automated Medical Reports—0
Regularized Contrastive Learning of Semantic Search—0
End-to-End Lyrics Recognition with Self-supervised Learning—0
Generalized Parametric Contrastive LearningCode2
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Whodunit? Learning to Contrast for Authorship AttributionCode0
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based RecommendationCode1
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning—0
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image SegmentationCode1
AVT: Audio-Video Transformer for Multimodal Action Recognition—0
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal GroundingCode1
SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning—0
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings LearningCode0
Contrastive Learning for Time Series on Dynamic Graphs—0
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial ImagesCode1
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach—0
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator—0
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive LearningCode1
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive LearningCode0
Non-Linguistic Supervision for Contrastive Learning of Sentence EmbeddingsCode1
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation—0
Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction—0
S^3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification—0
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
Few-Shot Classification with Contrastive Learning—0
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape CorrespondenceCode1
MetaMask: Revisiting Dimensional Confounder for Self-Supervised LearningCode1
Graph Contrastive Learning with Cross-view Reconstruction—0
Spatial-then-Temporal Self-Supervised Learning for Video CorrespondenceCode1
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection—0
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation—0
FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning—0
Graph Contrastive Learning with Personalized Augmentation—0
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding—0
Jointly Contrastive Representation Learning on Road Network and TrajectoryCode1
Joint Debiased Representation and Image Clustering Learning with Self-Supervision—0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial AttackCode0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning—0
Improving Self-Supervised Learning by Characterizing Idealized RepresentationsCode1
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity MatchingCode0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural FeaturesCode0
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive LearningCode0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
Ranking-Enhanced Unsupervised Sentence Representation LearningCode1
Information Maximization for Extreme Pose Face Recognition—0
Semi-supervised Crowd Counting via Density AgencyCode1
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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