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 4201–4225 of 6661 papers

TitleStatusHype
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning—0
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
Contrastive Data and Learning for Natural Language Processing—0
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation—0
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
Contrastive Disentangled Learning on Graph for Node Classification—0
Contrastive Document Representation Learning with Graph Attention Networks—0
Contrastive Domain Adaptation—0
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning—0
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data—0
Contrastive estimation reveals topic posterior information to linear models—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Feature Masking Open-Vocabulary Vision Transformer—0
Contrastive Federated Learning with Tabular Data Silos—0
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer—0
Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
Contrastive Graph Clustering in Curvature Spaces—0
Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores—0
Contrastive Graph Few-Shot Learning—0
Contrastive Graph Multimodal Model for Text Classification in Videos—0
Contrastive Graph Prompt-tuning for Cross-domain Recommendation—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—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