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

Showing 15761600 of 6661 papers

TitleStatusHype
3D Interaction Geometric Pre-training for Molecular Relational LearningCode1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
CRIS: CLIP-Driven Referring Image SegmentationCode1
Certifiably Robust Graph Contrastive LearningCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
Artistic Style Transfer with Internal-external Learning and Contrastive LearningCode1
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked AutoencodersCode1
COMPLETER: Incomplete Multi-view Clustering via Contrastive PredictionCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face SynthesisCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCoCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
PH-Net: Semi-Supervised Breast Lesion Segmentation via Patch-wise HardnessCode1
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT DenoisingCode1
Directed Graph Contrastive LearningCode1
Point-Level Region Contrast for Object Detection Pre-TrainingCode1
Pointly-supervised 3D Scene Parsing with Viewpoint BottleneckCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information MaximizationCode1
Cross-Domain Sentiment Classification with In-Domain Contrastive LearningCode1
PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit AugmentationsCode1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal LearningCode1
Cross-modal Contrastive Learning for Multimodal Fake News DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified