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 1201–1250 of 6661 papers

TitleStatusHype
Cross-modal Contrastive Learning for Multimodal Fake News DetectionCode1
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Beyond Co-occurrence: Multi-modal Session-based RecommendationCode1
Cross-Modal Contrastive Learning for Text-to-Image GenerationCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class DiscoveryCode1
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud RegistrationCode1
Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image PairsCode1
Contrastive Fine-grained Class Clustering via Generative Adversarial NetworksCode1
Alleviating Over-smoothing for Unsupervised Sentence RepresentationCode1
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic ImagesCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo LabellingCode1
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal ConversionCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time SeriesCode1
Towards Cross-Table Masked Pretraining for Web Data MiningCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Intent Contrastive Learning with Cross Subsequences for Sequential RecommendationCode1
Intent-guided Heterogeneous Graph Contrastive Learning for RecommendationCode1
Contrastive Grouping with Transformer for Referring Image SegmentationCode1
AD-CLIP: Adapting Domains in Prompt Space Using CLIPCode1
Conditional Contrastive Learning with KernelCode1
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance AnnotationCode1
Automated Spatio-Temporal Graph Contrastive LearningCode1
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
H2CGL: Modeling Dynamics of Citation Network for Impact PredictionCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine TranslationCode1
Data Augmenting Contrastive Learning of Speech Representations in the Time DomainCode1
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative AdversariesCode1
Contrastive Laplacian EigenmapsCode1
Contrastive Pretraining for Echocardiography Segmentation with Limited DataCode1
ISD: Self-Supervised Learning by Iterative Similarity DistillationCode1
Jigsaw Clustering for Unsupervised Visual Representation LearningCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
ConDA: Contrastive Domain Adaptation for AI-generated Text DetectionCode1
Contrastive Learning and Mixture of Experts Enables Precise Vector EmbeddingsCode1
Data Poisoning Attacks Against Multimodal EncodersCode1
Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport DistillationCode1
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D UnderstandingCode1
Joint Learning of Localized Representations from Medical Images and ReportsCode1
GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic SegmentationCode1
Alleviating Exposure Bias via Contrastive Learning for Abstractive Text SummarizationCode1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Debiased Contrastive Learning for Sequential RecommendationCode1
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving SystemsCode1
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed GraphsCode1
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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