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 1051–1100 of 6661 papers

TitleStatusHype
Graph Contrastive Learning for Anomaly DetectionCode1
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label ClassificationCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Generalizable Implicit Hate Speech Detection Using Contrastive LearningCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
ALSO: Automotive Lidar Self-supervision by Occupancy estimationCode1
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language ModelsCode1
GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with MaskingCode1
Contrastive Laplacian EigenmapsCode1
Contrastive Learning for Sports Video: Unsupervised Player ClassificationCode1
GAN-Control: Explicitly Controllable GANsCode1
Generalizable Synthetic Image Detection via Language-guided Contrastive LearningCode1
Generate to Understand for RepresentationCode1
Global Concept Explanations for Graphs by Contrastive LearningCode1
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor DetectorCode1
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation LearningCode1
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series ClassificationCode1
Frequency-Based Alignment of EEG and Audio Signals Using Contrastive Learning and SincNet for Auditory Attention DetectionCode1
Fragment-based Pretraining and Finetuning on Molecular GraphsCode1
Frequency Enhanced Hybrid Attention Network for Sequential RecommendationCode1
From Real to Cloned Singer IdentificationCode1
Contrastive Cross-domain Recommendation in MatchingCode1
FocusFace: Multi-task Contrastive Learning for Masked Face RecognitionCode1
Following the Autoregressive Nature of LLM Embeddings via Compression and AlignmentCode1
Contrastive Deep Nonnegative Matrix Factorization for Community DetectionCode1
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation DistillationCode1
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text EmbeddingsCode1
Contrastive Collaborative Filtering for Cold-Start Item RecommendationCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Frame-wise Action Representations for Long Videos via Sequence Contrastive LearningCode1
Free Lunch for Surgical Video Understanding by Distilling Self-SupervisionsCode1
Contrastive Fine-grained Class Clustering via Generative Adversarial NetworksCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Grouping with Transformer for Referring Image SegmentationCode1
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation TransferCode1
Contrastive ClusteringCode1
Contrastive Learning for Improving ASR Robustness in Spoken Language UnderstandingCode1
g3D-LF: Generalizable 3D-Language Feature Fields for Embodied TasksCode1
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait RecognitionCode1
Consistent Explanations by Contrastive LearningCode1
Consistent Representation Learning for Continual Relation ExtractionCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
Contrastive Learning and Mixture of Experts Enables Precise Vector EmbeddingsCode1
Contrastive Code Representation LearningCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
Learning the Unlearned: Mitigating Feature Suppression in Contrastive LearningCode1
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving SystemsCode1
Contrastive Deep SupervisionCode1
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent SpaceCode1
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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