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 2151–2200 of 6661 papers

TitleStatusHype
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention UnderstandingCode0
MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item DetectionCode0
DCL: Differential Contrastive Learning for Geometry-Aware Depth SynthesisCode0
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image SegmentationCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
Dataset Ownership Verification in Contrastive Pre-trained ModelsCode0
Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain TranslationCode0
Class Gradient Projection For Continual LearningCode0
Data-Efficient Image Recognition with Contrastive Predictive CodingCode0
Data Efficient Contrastive Learning in Histopathology using Active SamplingCode0
Mitigating Negative Style Transfer in Hybrid Dialogue SystemCode0
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine TranslationCode0
A Contrastive Learning Approach to Mitigate Bias in Speech ModelsCode0
Data Cleansing with Contrastive Learning for Vocal Note Event AnnotationsCode0
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud RegistrationCode0
Data Augmentation for Compositional Data: Advancing Predictive Models of the MicrobiomeCode0
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited LabelsCode0
CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous QueriesCode0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-trainingCode0
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimationCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Multi-task Meta Label Correction for Time Series PredictionCode0
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in SegmentationCode0
A Generalizable Deep Learning System for Cardiac MRICode0
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patientsCode0
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained AlignmentCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QACode0
Medical Question Summarization with Entity-driven Contrastive LearningCode0
Medication Recommendation via Dual Molecular Modalities and Multi-Step EnhancementCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive LossesCode0
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental PerceptionCode0
CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized CaptionsCode0
CTSM: Combining Trait and State Emotions for Empathetic Response ModelCode0
CTRLStruct: Dialogue Structure Learning for Open-Domain Response GenerationCode0
CKD: Contrastive Knowledge Distillation from A Sample-wise PerspectiveCode0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure ImageCode0
Masked Student Dataset of ExpressionsCode0
Mask-Guided Contrastive Attention Model for Person Re-IdentificationCode0
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category DiscoveryCode0
Mask-informed Deep Contrastive Incomplete Multi-view ClusteringCode0
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype AssociationsCode0
Masked Collaborative Contrast for Weakly Supervised Semantic SegmentationCode0
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive LearningCode0
Crowdsourcing and Evaluating Text-Based Audio Retrieval RelevancesCode0
Cross-view Semantic Alignment for Livestreaming Product RecognitionCode0
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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