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 3751–3775 of 6661 papers

TitleStatusHype
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Training Class-Imbalanced Diffusion Model Via Overlap OptimizationCode0
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning—0
Low-Rank Graph Contrastive Learning for Node Classification—0
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
Disambiguated Node Classification with Graph Neural NetworksCode0
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image AnalysisCode0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs—0
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction—0
Topic Modeling as Multi-Objective Contrastive Optimization—0
Contrastive Learning for Regression on Hyperspectral Data—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation—0
Rethinking Graph Masked Autoencoders through Alignment and UniformityCode0
Generalizing Conversational Dense Retrieval via LLM-Cognition Data AugmentationCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
Learning Contrastive Feature Representations for Facial Action Unit DetectionCode0
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning—0
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning—0
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation—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