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 4651–4700 of 6661 papers

TitleStatusHype
Cone: Unsupervised Contrastive Opinion ExtractionCode0
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive LearningCode0
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning—0
Model-Contrastive Federated Domain Adaptation—0
Contrastive Enhanced Slide Filter Mixer for Sequential RecommendationCode0
Weighted Point Cloud Normal Estimation—0
Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the DarkCode0
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer—0
Contrastive Learning for Low-light Raw Denoising—0
Contrastive Graph Clustering in Curvature Spaces—0
REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search ModelsCode0
Deep Unsupervised Learning for 3D ALS Point Cloud Change DetectionCode0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
Contrastive Learning for Sleep Staging based on Inter Subject CorrelationCode0
Multi-Domain Learning From Insufficient Annotations—0
Multi-Modality Deep Network for JPEG Artifacts Reduction—0
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction—0
Revisiting Graph Contrastive Learning for Anomaly DetectionCode0
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video—0
Forward-Forward Contrastive Learning—0
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive SummarizationCode0
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark—0
A Simplified Framework for Contrastive Learning for Node Representations—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
LostPaw: Finding Lost Pets using a Contrastive Learning-based Transformer with Visual InputCode0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information—0
ContraNeRF: 3D-Aware Generative Model via Contrastive Learning with Unsupervised Implicit Pose Embedding—0
MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive LearningCode0
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography—0
All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement—0
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds—0
Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints—0
Sample-Specific Debiasing for Better Image-Text Models—0
OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network—0
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation—0
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation—0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
Spectral Normalization and Dual Contrastive Regularization for Image-to-Image TranslationCode0
Unsupervised CD in satellite image time series by contrastive learning and feature tracking—0
Learn What NOT to Learn: Towards Generative Safety in Chatbots—0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning—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