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 4176–4200 of 6661 papers

TitleStatusHype
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot FillingCode0
Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning—0
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive LearningCode0
Visual Self-supervised Learning Scheme for Dense Prediction Tasks on X-ray Images—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
Splicing Up Your Predictions with RNA Contrastive LearningCode0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning—0
IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-trainingCode0
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition—0
A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News ArticlesCode0
The Solution for the CVPR2023 NICE Image Captioning Challenge—0
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval—0
Improving Contrastive Learning of Sentence Embeddings with Focal-InfoNCECode0
Contrastive Prompt Learning-based Code Search based on Interaction Matrix—0
Adaptive Multi-head Contrastive LearningCode0
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network—0
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift—0
Transferable Availability Poisoning AttacksCode0
SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment—0
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction—0
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation—0
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential RecommendationCode0
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation—0
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation—0
Perfect Alignment May be Poisonous to Graph Contrastive LearningCode0
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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