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 601–650 of 6661 papers

TitleStatusHype
Debiased Contrastive LearningCode1
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information RetrievalCode1
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class DiscoveryCode1
Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image PairsCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-TuningCode1
Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport DistillationCode1
CLIP-Event: Connecting Text and Images with Event StructuresCode1
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
Cluster-Level Contrastive Learning for Emotion Recognition in ConversationsCode1
Data Augmenting Contrastive Learning of Speech Representations in the Time DomainCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
Adaptive Soft Contrastive LearningCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine TranslationCode1
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative AdversariesCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Data Poisoning Attacks Against Multimodal EncodersCode1
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive LearningCode1
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging DataCode1
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time SeriesCode1
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIPCode1
Biomedical Entity Linking with Contrastive Context MatchingCode1
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and PatientsCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at ScaleCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Anatomical Foundation Models for Brain MRIsCode1
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive LearningCode1
Cluster-guided Contrastive Graph Clustering NetworkCode1
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
Black-Box Attack against GAN-Generated Image Detector with Contrastive PerturbationCode1
CLARA: Multilingual Contrastive Learning for Audio Representation AcquisitionCode1
Black Box Few-Shot Adaptation for Vision-Language modelsCode1
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic SegmentationCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance AnnotationCode1
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
Co2L: Contrastive Continual LearningCode1
Co^2L: Contrastive Continual LearningCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
Debiased Contrastive Learning for Sequential RecommendationCode1
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive LearningCode1
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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