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 5101–5125 of 6661 papers

TitleStatusHype
Semi-supervised Semantic Segmentation with Error Localization NetworkCode1
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition—0
Rethinking Visual Geo-localization for Large-Scale ApplicationsCode2
Lip to Speech Synthesis with Visual Context Attentional GANCode1
Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
POS-BERT: Point Cloud One-Stage BERT Pre-TrainingCode1
Bayesian Negative Sampling for RecommendationCode0
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment AnalysisCode0
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks—0
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity RecognitionCode0
Transformer-Empowered Content-Aware Collaborative Filtering—0
Automatic Biomedical Term Clustering by Learning Fine-grained Term RepresentationsCode1
Graph Enhanced Contrastive Learning for Radiology Findings SummarizationCode1
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense EmbeddingsCode1
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation—0
Marginal Contrastive Correspondence for Guided Image Generation—0
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt TuningCode0
Video-Text Representation Learning via Differentiable Weak Temporal AlignmentCode1
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification—0
Semantic Pose Verification for Outdoor Visual Localization with Self-supervised Contrastive Learning—0
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval—0
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action LocalizationCode1
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCoCode1
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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