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 5776–5800 of 6661 papers

TitleStatusHype
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Multi-Modal CLIP-Informed Protein Editing—0
Multimodal Contrastive In-Context Learning—0
Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables—0
Multimodal Contrastive Learning and Tabular Attention for Automated Alzheimer's Disease Prediction—0
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata—0
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications—0
Multimodal contrastive learning for remote sensing tasks—0
Multi-modal Contrastive Learning for Tumor-specific Missing Modality Synthesis—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery—0
Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition—0
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts—0
Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets—0
Multimodal Generalized Category Discovery—0
Multimodal generative semantic communication based on latent diffusion model—0
Multimodal Graph Constrastive Learning and Prompt for ChartQA—0
Adversarial Defense Framework for Graph Neural Network—0
Multi-Modality Deep Network for JPEG Artifacts Reduction—0
Multi-Modality Driven LoRA for Adverse Condition Depth Estimation—0
Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation—0
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning—0
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation—0
Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval—0
Multimodal Representation Learning using Adaptive Graph Construction—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