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 2376–2400 of 6661 papers

TitleStatusHype
CycleCL: Self-supervised Learning for Periodic Videos—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
A contrastive-learning approach for auditory attention detection—0
Graph Information Bottleneck for Remote Sensing Segmentation—0
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset—0
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain—0
Cut the CARP: Fishing for zero-shot story evaluation—0
ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation—0
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning—0
CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization—0
A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation—0
Curriculum Learning for Data-Efficient Vision-Language Alignment—0
CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning—0
A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization—0
3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation—0
CUPR: Contrastive Unsupervised Learning for Person Re-identification—0
A Framework using Contrastive Learning for Classification with Noisy Labels—0
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
CktGen: Specification-Conditioned Analog Circuit Generation—0
Graph Contrastive Learning with Generative Adversarial Network—0
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios—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