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 401–450 of 6661 papers

TitleStatusHype
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-IdentificationCode1
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment AnalysisCode1
A Self-supervised Method for Entity AlignmentCode1
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-IdentificationCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
PITN: Physics-Informed Temporal Networks for Cuffless Blood Pressure EstimationCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
Contrastive Learning with Hard Negative Entities for Entity Set ExpansionCode1
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence EmbeddingsCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive LearningCode1
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect DetectionCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
CLARA: Multilingual Contrastive Learning for Audio Representation AcquisitionCode1
A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationCode1
A simple, efficient and scalable contrastive masked autoencoder for learning visual representationsCode1
Contrastive Learning with Hard Negative SamplesCode1
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information RetrievalCode1
CoSQA: 20,000+ Web Queries for Code Search and Question AnsweringCode1
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised LearningCode1
A Simple Long-Tailed Recognition Baseline via Vision-Language ModelCode1
CLDG: Contrastive Learning on Dynamic GraphsCode1
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation ExtractionCode1
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-TuningCode1
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive LearningCode1
CLEVE: Contrastive Pre-training for Event ExtractionCode1
CP2: Copy-Paste Contrastive Pretraining for Semantic SegmentationCode1
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based LossesCode1
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language AlignmentCode1
Reinforcement Learning Friendly Vision-Language Model for MinecraftCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
Assisting Mathematical Formalization with A Learning-based Premise RetrieverCode1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
CLIP-KD: An Empirical Study of CLIP Model DistillationCode1
AstroCLIP: A Cross-Modal Foundation Model for GalaxiesCode1
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive LearningCode1
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion RecognitionCode1
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment DetectionCode1
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural NetworksCode1
CL-MVSNet: Unsupervised Multi-View Stereo with Dual-Level Contrastive LearningCode1
A graph-transformer for whole slide image classificationCode1
Cross-Domain Sentiment Classification with In-Domain Contrastive LearningCode1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
Cross-modal Causal Relation Alignment for Video Question GroundingCode1
A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place RecognitionCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Cluster-Level Contrastive Learning for Emotion Recognition in ConversationsCode1
C3: Cross-instance guided Contrastive ClusteringCode1
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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