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 301–350 of 6661 papers

TitleStatusHype
Context-self contrastive pretraining for crop type semantic segmentationCode1
Continuous Contrastive Learning for Long-Tailed Semi-Supervised RecognitionCode1
Temporal Context Aggregation for Video Retrieval with Contrastive LearningCode1
Continuous Learning for Android Malware DetectionCode1
Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image TranslationCode1
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment AnalysisCode1
Constructing Tree-based Index for Efficient and Effective Dense RetrievalCode1
A Simple Long-Tailed Recognition Baseline via Vision-Language ModelCode1
Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive LearningCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive LearningCode1
ContraBAR: Contrastive Bayes-Adaptive Deep RLCode1
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text EmbeddingsCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation TransferCode1
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
Adversarial Self-Supervised Contrastive LearningCode1
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal GroundingCode1
Consistent Explanations by Contrastive LearningCode1
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation ExtractionCode1
Assisting Mathematical Formalization with A Learning-based Premise RetrieverCode1
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
A Simple Graph Contrastive Learning Framework for Short Text ClassificationCode1
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
Consistent Representation Learning for Continual Relation ExtractionCode1
ContraCLM: Contrastive Learning For Causal Language ModelCode1
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving SystemsCode1
Contrastive Mean Teacher for Domain Adaptive Object DetectorsCode1
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based FeaturesCode1
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity RecognitionCode1
Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex InteractionsCode1
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural NetworksCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
COMPLETER: Incomplete Multi-view Clustering via Contrastive PredictionCode1
Compositional Exemplars for In-context LearningCode1
Adversarial Graph Augmentation to Improve Graph Contrastive LearningCode1
3D Human Pose, Shape and Texture from Low-Resolution Images and VideosCode1
CoMatch: Semi-supervised Learning with Contrastive Graph RegularizationCode1
A Closer Look at Self-Supervised Lightweight Vision TransformersCode1
Adversarial Examples Are Not Real FeaturesCode1
A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationCode1
Community-Invariant Graph Contrastive LearningCode1
Compressive Visual RepresentationsCode1
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive LearningCode1
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited AnnotationsCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural NetworksCode1
3D Human Action Representation Learning via Cross-View Consistency PursuitCode1
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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