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 4251–4275 of 6661 papers

TitleStatusHype
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
Contrastive Learning for Cross-modal Artist Retrieval—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking—0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Contrastive Learning for Fair Representations—0
Contrastive Learning for Fair Representations—0
Contrastive Learning for Image Captioning—0
Contrastive Learning for Interactive Recommendation in Fashion—0
Contrastive Learning for Label-Efficient Semantic Segmentation—0
Contrastive Learning for Label Efficient Semantic Segmentation—0
Contrastive Learning for Lifted Networks—0
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
Contrastive Learning for Low-light Raw Denoising—0
Contrastive Learning for Low Resource Machine Translation—0
Contrastive Learning for Mitochondria Segmentation—0
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views—0
Contrastive Learning for Recommender System—0
Contrastive Learning for Regression on Hyperspectral Data—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Contrastive Learning for Time Series on Dynamic Graphs—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