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 4276–4300 of 6661 papers

TitleStatusHype
Contrastive Learning for Unsupervised Image-to-Image Translation—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
Contrastive Learning for Unsupervised Video Highlight Detection—0
Contrastive Learning for View Classification of Echocardiograms—0
Contrastive Learning from Demonstrations—0
Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation—0
Contrastive Learning from Pairwise Measurements—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients—0
Contrastive Learning in Memristor-based Neuromorphic Systems—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—0
Contrastive learning, multi-view redundancy, and linear models—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
Contrastive Learning of Coarse-Grained Force Fields—0
Contrastive Learning of Emoji-based Representations for Resource-Poor Languages—0
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge—0
Contrastive Learning of Features between Images and LiDAR—0
Contrastive Learning of Global and Local Video Representations—0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency—0
Contrastive Learning of Natural Language and Code Representations for Semantic Code Search—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