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 3701–3725 of 6661 papers

TitleStatusHype
All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning—0
A Low Rank Promoting Prior for Unsupervised Contrastive Learning—0
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps—0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection—0
A Mathematical Perspective On Contrastive Learning—0
AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning—0
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction—0
A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation—0
A Mutual Information Perspective on Federated Contrastive Learning—0
A Mutually Reinforced Framework for Pretrained Sentence Embeddings—0
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation—0
An Adaptive Contrastive Learning Model for Spike Sorting—0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Analysis of Using Sigmoid Loss for Contrastive Learning—0
Analyzing Local Representations of Self-supervised Vision Transformers—0
Analyzing Multimodal Objectives Through the Lens of Generative Diffusion Guidance—0
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding—0
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing—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