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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2551–2600 of 6661 papers

TitleStatusHype
Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learning—0
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
On the Difficulty of Defending Contrastive Learning against Backdoor Attacks—0
Incomplete Contrastive Multi-View Clustering with High-Confidence GuidingCode0
TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-trainingCode0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)—0
Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)—0
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge GraphsCode0
Patch-wise Graph Contrastive Learning for Image TranslationCode1
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel ObjectsCode4
Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches—0
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation—0
Domain Prompt Learning with Quaternion Networks—0
Toward Real Text Manipulation Detection: New Dataset and New SolutionCode1
Supervised Contrastive Learning for Fine-grained Chromosome Recognition—0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationCode0
Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment RetrievalCode0
Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive LearningCode1
Hallucination Augmented Contrastive Learning for Multimodal Large Language ModelCode1
NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide ImagesCode0
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms—0
RCA-NOC: Relative Contrastive Alignment for Novel Object Captioning—0
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks—0
Mining Gaze for Contrastive Learning toward Computer-Assisted DiagnosisCode1
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity—0
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting—0
GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with MaskingCode1
Temporal Supervised Contrastive Learning for Modeling Patient Risk ProgressionCode0
Weakly Supervised Video Individual CountingWeakly Supervised Video Individual CountingCode1
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental ClusteringCode0
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models—0
StructComp: Substituting Propagation with Structural Compression in Training Graph Contrastive LearningCode0
Understanding Community Bias Amplification in Graph Representation Learning—0
Damage GAN: A Generative Model for Imbalanced Data—0
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations—0
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn GraphsCode0
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series—0
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds—0
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift—0
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry—0
Rethinking and Simplifying Bootstrapped Graph LatentsCode0
Graph Information Bottleneck for Remote Sensing Segmentation—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation—0
Show:102550
← PrevPage 52 of 134Next →
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