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 4401–4425 of 6661 papers

TitleStatusHype
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation—0
Counter-Contrastive Learning for Language GANs—0
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Counting Objects in a Robotic Hand—0
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning—0
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI—0
Cross-Age Contrastive Learning for Age-Invariant Face Recognition—0
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning—0
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations—0
Cross-Domain 3D Hand Pose Estimation With Dual Modalities—0
Cross-Domain Document Layout Analysis Using Document Style Guide—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
Cross-Lingual IPA Contrastive Learning for Zero-Shot NER—0
Cross-Lingual Multi-Hop Knowledge Editing -- Benchmarks, Analysis and a Simple Contrastive Learning based Approach—0
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning—0
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning—0
Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop—0
Cross-modal Contrastive Learning for Speech Translation—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