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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 4801–4850 of 6661 papers

TitleStatusHype
Supervised Contrastive Learning Approach for Contextual Ranking—0
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models—0
Supervised Contrastive Learning for Recommendation—0
Supervised Contrastive Learning for Accented Speech Recognition—0
Supervised contrastive learning for cell stage classification of animal embryos—0
Supervised Contrastive Learning for Cross-lingual Transfer Learning—0
Supervised Contrastive Learning for Fine-grained Chromosome Recognition—0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing—0
Supervised contrastive learning from weakly-labeled audio segments for musical version matching—0
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition—0
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus—0
Supervised Contrastive Learning with Structure Inference for Graph Classification—0
Supervised Contrastive Learning with Heterogeneous Similarity for Distribution Shifts—0
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition—0
Supervised Contrastive Learning with Tree-Structured Parzen Estimator Bayesian Optimization for Imbalanced Tabular Data—0
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network—0
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification—0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Supervised Graph Contrastive Pretraining for Text Classification—0
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning—0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Support-Set Based Cross-Supervision for Video Grounding—0
Support-set bottlenecks for video-text representation learning—0
Surveillance Face Anti-spoofing—0
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese—0
Survey of Loss Augmented Knowledge Tracing—0
SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation—0
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval—0
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings—0
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image—0
Syfer: Neural Obfuscation for Private Data Release—0
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations—0
SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining—0
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation—0
Syntax-guided Contrastive Learning for Pre-trained Language Model—0
Synthetic Hard Negative Samples for Contrastive Learning—0
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data—0
TabGSL: Graph Structure Learning for Tabular Data Prediction—0
Table-based Fact Verification with Self-labeled Keypoint Alignment—0
Tac2Pose: Tactile Object Pose Estimation from the First Touch—0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
Tackling Online One-Class Incremental Learning by Removing Negative Contrasts—0
Deeper-GXX: Deepening Arbitrary GNNs—0
CleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning—0
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning—0
TaCo: Textual Attribute Recognition via Contrastive Learning—0
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment—0
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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