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 4101–4150 of 6661 papers

TitleStatusHype
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Code Representation Learning At Scale—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
CognitiveNet: Enriching Foundation Models with Emotions and Awareness—0
Co-guiding for Multi-intent Spoken Language Understanding—0
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving—0
COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography—0
CoKe: Localized Contrastive Learning for Robust Keypoint Detection—0
Collaborative Contrastive Network for Click-Through Rate Prediction—0
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection—0
Collaborative Visual Place Recognition through Federated Learning—0
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing—0
Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation—0
Combined Scaling for Zero-shot Transfer Learning—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
Pre-Training Representations of Binary Code Using Contrastive Learning—0
ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model—0
CommitBART: A Large Pre-trained Model for GitHub Commits—0
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training—0
Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images—0
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift—0
Composable Augmentation Encoding for Video Representation Learning—0
Composite Concept Extraction through Backdooring—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Compressed Video Contrastive Learning—0
Computer User Interface Understanding. A New Dataset and a Learning Framework—0
Conan-embedding: General Text Embedding with More and Better Negative Samples—0
ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection—0
CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesis—0
Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning—0
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation—0
ConIsI: A Contrastive Framework with Inter-sentence Interaction for Self-supervised Sentence Representation—0
ConKeD++ -- Improving descriptor learning for retinal image registration: A comprehensive study of contrastive losses—0
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration—0
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation—0
ConPCO: Preserving Phoneme Characteristics for Automatic Pronunciation Assessment Leveraging Contrastive Ordinal Regularization—0
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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