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 2051–2075 of 6661 papers

TitleStatusHype
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—0
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation LearningCode0
CLHA: A Simple yet Effective Contrastive Learning Framework for Human AlignmentCode0
Toward Open-Set Human Object Interaction DetectionCode0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting—0
Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning—0
Knowledge-aware Dual-side Attribute-enhanced RecommendationCode0
Towards Channel-Resilient CSI-Based RF Fingerprinting using Deep Learning—0
EAGLE: A Domain Generalization Framework for AI-generated Text Detection—0
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations—0
CTSM: Combining Trait and State Emotions for Empathetic Response ModelCode0
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
Contrastive Learning on Multimodal Analysis of Electronic Health Records—0
Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation—0
Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks—0
FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos—0
InternVideo2: Scaling Foundation Models for Multimodal Video UnderstandingCode7
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt SynergyCode7
Unsupervised Audio-Visual Segmentation with Modality Alignment—0
Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting—0
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity LearningCode1
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence RetrievalCode0
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning—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