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 2551–2600 of 6661 papers

TitleStatusHype
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
Learning Semi-Supervised Medical Image Segmentation from Spatial RegistrationCode0
ACCIO: Table Understanding Enhanced via Contrastive Learning with AggregationsCode0
Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from SpeechCode0
Learning Street View Representations with Spatiotemporal ContrastCode0
Contrastive Learning and Adversarial Disentanglement for Task-Oriented Semantic CommunicationsCode0
PARDON: Privacy-Aware and Robust Federated Domain GeneralizationCode0
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text ClassificationCode0
Learning Node Representations against PerturbationsCode0
Analyzing Data-Centric Properties for Graph Contrastive LearningCode0
Contrastive Latent Variable Models for Neural Text GenerationCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus ImagesCode0
Learning Oculomotor Behaviors from ScanpathCode0
Learning Multimodal Volumetric Features for Large-Scale Neuron TracingCode0
Learning Representations for Clustering via Partial Information Discrimination and Cross-Level InteractionCode0
Learning Invariance from Generated Variance for Unsupervised Person Re-identificationCode0
Bilateral Personalized Dialogue Generation with Contrastive LearningCode0
Contrastive Learning for Inference in DialogueCode0
Learning Label Hierarchy with Supervised Contrastive LearningCode0
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identificationCode0
Learning Text Similarity with Siamese Recurrent NetworksCode0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn GraphsCode0
Contrastive Knowledge Graph Error DetectionCode0
Contrastive Instruction-Trajectory Learning for Vision-Language NavigationCode0
Feature-Level Debiased Natural Language UnderstandingCode0
Self-supervised Feature-Gate Coupling for Dynamic Network PruningCode0
Learning Graph Augmentations to Learn Graph RepresentationsCode0
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR ScansCode0
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient AlignmentCode0
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium SegmentationCode0
Learning Discriminative Visual-Text Representation for Polyp Re-IdentificationCode0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised RankingCode0
Learning Contrastive Feature Representations for Facial Action Unit DetectionCode0
Analysing the Robustness of Dual Encoders for Dense Retrieval Against MisspellingsCode0
Latent Processes Identification From Multi-View Time SeriesCode0
Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced RecommendationCode0
FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical LearningCode0
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation LearningCode0
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation ExtractionCode0
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
Fairness-aware Multi-view ClusteringCode0
ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian SplattingCode0
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer FeaturesCode0
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive LearningCode0
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Large-Scale Hyperspectral Image Clustering Using Contrastive 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