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 5376–5400 of 6661 papers

TitleStatusHype
Learn to Discover Dialog Intents via Self-supervised Context Pretraining—0
Learning Monolingual Sentence Embeddings with Large-scale Parallel Translation Datasets—0
HCL-MTC Hierarchical Contrastive Learning for Multi-label Text Classification—0
CLoCE:Contrastive Learning Optimize Continous Prompt Embedding Space in Relation Extraction—0
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning—0
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative SamplesCode1
Contrastive Pretraining for Echocardiography Segmentation with Limited DataCode1
Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identificationCode1
Contrastive Laplacian EigenmapsCode1
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data—0
Multi-task Pre-training Language Model for Semantic Network CompletionCode0
CLIP-Event: Connecting Text and Images with Event StructuresCode1
PromptBERT: Improving BERT Sentence Embeddings with PromptsCode2
Robust Contrastive Learning against Noisy ViewsCode1
Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning—0
Motion-Focused Contrastive Learning of Video RepresentationsCode1
Feature Extraction Framework based on Contrastive Learning with Adaptive Positive and Negative Samples—0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma GradingCode0
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Supervised Contrastive Learning for Recommendation—0
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping—0
Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents—0
On the Effectiveness of Sampled Softmax Loss for Item Recommendation—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