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 2901–2925 of 6661 papers

TitleStatusHype
Boundary-Aware Proposal Generation Method for Temporal Action Localization—0
Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation—0
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning—0
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems—0
Contrastive Prompt Learning-based Code Search based on Interaction Matrix—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
Exploiting Auxiliary Caption for Video Grounding—0
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text—0
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge—0
Heterogeneous Temporal Hypergraph Neural Network—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning—0
HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Generating Compositional Color Representations from Text—0
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings—0
Contrastive Learning of Emoji-based Representations for Resource-Poor Languages—0
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation—0
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery—0
General-Purpose Multi-Modal OOD Detection Framework—0
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation—0
Generalizing Supervised Contrastive learning: A Projection Perspective—0
Hierarchical Banzhaf Interaction for General Video-Language Representation Learning—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Contrastive Learning of Coarse-Grained Force Fields—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