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 4026–4050 of 6661 papers

TitleStatusHype
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data—0
SCStory: Self-supervised and Continual Online Story DiscoveryCode0
Robust Basket Recommendation via Noise-tolerated Graph Contrastive LearningCode0
BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights—0
Text2Loc: 3D Point Cloud Localization from Natural LanguageCode0
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation—0
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance—0
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis—0
Class Gradient Projection For Continual LearningCode0
Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning—0
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning—0
Incorporating granularity bias as the margin into contrastive loss for video captioning—0
Weakly-Supervised Audio-Visual Segmentation—0
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses—0
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning—0
Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment—0
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering—0
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning—0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
Co-guiding for Multi-intent Spoken Language Understanding—0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection—0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive LearningCode0
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive LearningCode0
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series—0
Unearthing Common Inconsistency for Generalisable Deepfake 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