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 2926–2950 of 6661 papers

TitleStatusHype
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Generating Compositional Color Representations from Text—0
Contrastive Learning of Emoji-based Representations for Resource-Poor Languages—0
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
Hierarchical Cross Contrastive Learning of Visual Representations—0
Hierarchical discriminative learning improves visual representations of biomedical microscopy—0
General-Purpose Multi-Modal OOD Detection Framework—0
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities—0
Generalizing Supervised Contrastive learning: A Projection Perspective—0
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning—0
Contrastive Learning of Coarse-Grained Force Fields—0
Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval—0
Generalizing Graph ODE for Learning Complex System Dynamics across Environments—0
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity—0
BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning—0
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
Contrastive learning, multi-view redundancy, and linear models—0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Generalized Class Discovery in Instance Segmentation—0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—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