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 5101–5125 of 6661 papers

TitleStatusHype
Hebbian-Descent—0
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data—0
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach—0
HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning—0
Heterogeneous bimodal attention fusion for speech emotion recognition—0
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—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
Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media—0
Heterogeneous Temporal Hypergraph Neural Network—0
Heterophilous Distribution Propagation for Graph Neural Networks—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
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings—0
Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents—0
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation—0
Hierarchical Banzhaf Interaction for General Video-Language Representation Learning—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network—0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
Hierarchical Cross Contrastive Learning of Visual Representations—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