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 5501–5550 of 6661 papers

TitleStatusHype
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
Automatic retrieval of corresponding US views in longitudinal examinationsCode0
Molecular Graph Contrastive Learning with Line GraphCode0
TopoGCL: Topological Graph Contrastive LearningCode0
Accommodating Audio Modality in CLIP for Multimodal ProcessingCode0
DistillCSE: Distilled Contrastive Learning for Sentence EmbeddingsCode0
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive LearningCode0
Dissecting Supervised Contrastive LearningCode0
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual AlignmentCode0
MaCLR: Motion-aware Contrastive Learning of Representations for VideosCode0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal VideosCode0
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation LearningCode0
Disentangling Policy from Offline Task Representation Learning via Adversarial Data AugmentationCode0
Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive LearningCode0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling LawsCode0
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action RecognitionCode0
Self-supervised Vision Transformers for 3D Pose Estimation of Novel ObjectsCode0
Self-supervised Visualisation of Medical Image DatasetsCode0
Model Editing for LLMs4Code: How Far are We?Code0
Model-Contrastive Learning for Backdoor DefenseCode0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
Disentangled Modeling of Preferences and Social Influence for Group RecommendationCode0
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and SegmentationCode0
Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge TracingCode0
Disentangled Contrastive Learning for Social RecommendationCode0
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual AlignmentCode0
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image SegmentationCode0
ToThePoint: Efficient Contrastive Learning of 3D Point Clouds via RecyclingCode0
MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item DetectionCode0
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature AlignmentCode0
Disentangled Contrastive Learning for Learning Robust Textual RepresentationsCode0
Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain TranslationCode0
AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided DiagnosisCode0
Mitigating Negative Style Transfer in Hybrid Dialogue SystemCode0
Contrastive Learning-based Chaining-Cluster for Multilingual Voice-Face AssociationCode0
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation LearningCode0
Discriminative Representation learning via Attention-Enhanced Contrastive Learning for Short Text ClusteringCode0
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine TranslationCode0
Discovering Global False Negatives On the Fly for Self-supervised Contrastive LearningCode0
Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced RecommendationCode0
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud RegistrationCode0
Semantic Contrastive Bootstrapping for Single-positive Multi-label RecognitionCode0
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited LabelsCode0
Semantic Information in Contrastive LearningCode0
SemanticMIM: Marring Masked Image Modeling with Semantics Compression for General Visual RepresentationCode0
Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive LossesCode0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
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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