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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5476–5500 of 6661 papers

TitleStatusHype
Pixel-level Correspondence for Self-Supervised Learning from Video—0
Few-Example Clustering via Contrastive Learning—0
Supervised Contrastive Learning Approach for Contextual Ranking—0
Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization—0
Unsupervised Learning for Human Sensing Using Radio Signals—0
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning—0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching—0
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image SegmentationCode0
Dynamic Contrastive Distillation for Image-Text Retrieval—0
Positive-Negative Equal Contrastive Loss for Semantic Segmentation—0
Multi-granularity Item-based Contrastive Recommendation—0
Game State Learning via Game Scene Augmentation—0
Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation—0
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
Contrastive Data and Learning for Natural Language Processing—0
PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts—0
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity RecognitionCode0
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce—0
Studying the impact of magnitude pruning on contrastive learning methodsCode0
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
Show:102550
← PrevPage 220 of 267Next →
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