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 4051–4075 of 6661 papers

TitleStatusHype
Clinically Labeled Contrastive Learning for OCT Biomarker Classification—0
ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish—0
CLIP Adaptation by Intra-modal Overlap Reduction—0
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting—0
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction—0
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media—0
CLLD: Contrastive Learning with Label Distance for Text Classification—0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning—0
CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss—0
CLoCE:Contrastive Learning Optimize Continous Prompt Embedding Space in Relation Extraction—0
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations—0
CLOUD: Contrastive Learning of Unsupervised Dynamics—0
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—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