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 5401–5450 of 6661 papers

TitleStatusHype
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection—0
Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization—0
Self-supervised Remote Sensing Images Change Detection at Pixel-level—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction—0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
Multi-view Fake News Detection Model Based on Dynamic Hypergraph—0
Multi-view Feature Extraction based on Dual Contrastive Head—0
Multi-view Feature Extraction based on Triple Contrastive Heads—0
Multi-view Granular-ball Contrastive Clustering—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Multi-View Pre-Trained Model for Code Vulnerability Identification—0
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
Music Era Recognition Using Supervised Contrastive Learning and Artist Information—0
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis—0
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading—0
Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification—0
MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation—0
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning—0
A Contrastive Learning Approach for Training Variational Autoencoder Priors—0
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos—0
Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries—0
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning—0
Negative Metric Learning for Graphs—0
Negative Prototypes Guided Contrastive Learning for WSOD—0
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition—0
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation—0
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation—0
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings—0
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime PredictionCode0
MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention HeadsCode0
Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence GenerationCode0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategiesCode0
Co-modeling the Sequential and Graphical Routes for Peptide Representation LearningCode0
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite ImageryCode0
Self-supervised Product Quantization for Deep Unsupervised Image RetrievalCode0
Self-supervised pseudo-colorizing of masked cellsCode0
Video-Language Critic: Transferable Reward Functions for Language-Conditioned RoboticsCode0
Motifs-based Recommender System via Hypergraph Convolution and Contrastive LearningCode0
MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant FeaturesCode0
Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in CodenamesCode0
MSVQ: Self-Supervised Learning with Multiple Sample Views and QueuesCode0
MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small DatasetsCode0
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image SegmentationCode0
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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