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 5501–5525 of 6661 papers

TitleStatusHype
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation ExtractionCode0
RCL: Relation Contrastive Learning for Zero-Shot Relation ExtractionCode0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning—0
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based PredictionCode0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection—0
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations—0
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT VolumesCode0
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
Interventional Contrastive Learning with Meta Semantic Regularizer—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)—0
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark—0
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation—0
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration—0
Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images AnalysisCode0
Geometry Contrastive Learning on Heterogeneous GraphsCode0
Graph Component Contrastive Learning for Concept Relatedness EstimationCode0
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis—0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning—0
Contrastive Learning of Features between Images and LiDAR—0
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training—0
CoSP: Co-supervised pretraining of pocket and ligand—0
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing—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