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 2551–2600 of 6661 papers

TitleStatusHype
Multi-view Granular-ball Contrastive Clustering—0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood ContextCode0
Cluster-guided Contrastive Class-imbalanced Graph Classification—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection—0
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models—0
UniLoc: Towards Universal Place Recognition Using Any Single Modality—0
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRecCode0
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced RegressionCode0
Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes—0
Personalized LLM for Generating Customized Responses to the Same Query from Different UsersCode0
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation—0
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference—0
Generalization Analysis for Deep Contrastive Representation Learning—0
Exploring Temporal Event Cues for Dense Video Captioning in Cyclic Co-learning—0
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models—0
Multi-Graph Co-Training for Capturing User Intent in Session-based RecommendationCode0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning ModelCode0
CATALOG: A Camera Trap Language-guided Contrastive Learning ModelCode0
MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow—0
TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes—0
A dual contrastive framework—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
ExeChecker: Where Did I Go Wrong?Code0
DomCLP: Domain-wise Contrastive Learning with Prototype Mixup for Unsupervised Domain GeneralizationCode0
Single-View Graph Contrastive Learning with Soft Neighborhood AwarenessCode0
Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification—0
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy—0
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?Code0
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—0
Multi-level Matching Network for Multimodal Entity LinkingCode0
jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images—0
Dynamic Modality-Camera Invariant Clustering for Unsupervised Visible-Infrared Person Re-identification—0
Learning Self-Supervised Audio-Visual Representations for Sound Recommendations—0
Multi-Scale Contrastive Learning for Video Temporal Grounding—0
Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation—0
Fine-grained Text to Image Synthesis—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning—0
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence—0
StyleMaster: Stylize Your Video with Artistic Generation and Translation—0
AmCLR: Unified Augmented Learning for Cross-Modal RepresentationsCode0
Image Retrieval with Intra-Sweep Representation Learning for Neck Ultrasound Scanning Guidance—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Measuring Pre-training Data Quality without Labels for Time Series Foundation Models—0
A Self-Learning Multimodal Approach for Fake News Detection—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