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 1001–1050 of 6661 papers

TitleStatusHype
Aligning Pretraining for Detection via Object-Level Contrastive LearningCode1
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited AnnotationsCode1
Rethinking the Paradigm of Content Constraints in Unpaired Image-to-Image TranslationCode1
A Message Passing Perspective on Learning Dynamics of Contrastive LearningCode1
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-TuningCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Aligning Text to Image in Diffusion Models is Easier Than You ThinkCode1
Contrastive Learning for Sports Video: Unsupervised Player ClassificationCode1
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive LearningCode1
Low-rank Prompt Interaction for Continual Vision-Language RetrievalCode1
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked AutoencodersCode1
LuSeg: Efficient Negative and Positive Obstacles Segmentation via Contrast-Driven Multi-Modal Feature Fusion on the LunarCode1
ReMeDi: Resources for Multi-domain, Multi-service, Medical DialoguesCode1
MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement LearningCode1
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage SummarizationCode1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
Constructing Tree-based Index for Efficient and Effective Dense RetrievalCode1
MAKE: Multi-Aspect Knowledge-Enhanced Vision-Language Pretraining for Zero-shot Dermatological AssessmentCode1
CRIS: CLIP-Driven Referring Image SegmentationCode1
CoMatch: Semi-supervised Learning with Contrastive Graph RegularizationCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image SegmentationCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic EnhancementCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language AlignmentCode1
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
CP2: Copy-Paste Contrastive Pretraining for Semantic SegmentationCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud RegistrationCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Bag of Instances Aggregation Boosts Self-supervised DistillationCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive LearningCode1
CoSQA: 20,000+ Web Queries for Code Search and Question AnsweringCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Correspondence Matters for Video Referring Expression ComprehensionCode1
Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious CorrelationsCode1
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity RecognitionCode1
CoRTX: Contrastive Framework for Real-time ExplanationCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
Adaptive Graph Contrastive Learning for RecommendationCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Learning the Unlearned: Mitigating Feature Suppression in Contrastive LearningCode1
Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph CompletionCode1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
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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