SOTAVerified

Self-Supervised Learning

Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration

Image source: LeCun

Papers

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Showing 2951–3000 of 5044 papers

TitleStatusHype
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
An Open-World Simulated Environment for Developmental Robotics—0
Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity—0
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning—0
A Novel Garment Transfer Method Supervised by Distilled Knowledge of Virtual Try-on Model—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features—0
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models—0
A Plasticity-Aware Method for Continual Self-Supervised Learning in Remote Sensing—0
Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures—0
Application of Tensorized Neural Networks for Cloud Classification—0
Applying General Turn-taking Models to Conversational Human-Robot Interaction—0
Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Games—0
A Pre-training Framework that Encodes Noise Information for Speech Quality Assessment—0
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC—0
A Probabilistic Model for Self-Supervised Learning—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
Are all negatives created equal in contrastive instance discrimination?—0
Are foundation models efficient for medical image segmentation?—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images—0
A Review on Deep Learning Techniques for Video Prediction—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
A Review on Objective-Driven Artificial Intelligence—0
A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations—0
A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models—0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation—0
A self-supervised framework for learning whole slide representations—0
A Self-Supervised Framework for Space Object Behaviour Characterisation—0
A Self-supervised GAN for Unsupervised Few-shot Object Recognition—0
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping—0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
A Self-Supervised Learning of a Foundation Model for Analog Layout Design Automation—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels—0
A self-supervised text-vision framework for automated brain abnormality detection—0
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys—0
A-SFS: Semi-supervised Feature Selection based on Multi-task Self-supervision—0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
A simple baseline for domain adaptation using rotation prediction—0
A Simple HMM with Self-Supervised Representations for Phone Segmentation—0
A Simplified Framework for Contrastive Learning for Node Representations—0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
ASR Bundestag: A Large-Scale political debate dataset in German—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
Assessing the State of Self-Supervised Human Activity Recognition using Wearables—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
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All datasetsDABSSTL-10CIFAR10cifar100ImageNet-100 (TEMI Split)TinyImageNetCIFAR-10CIFAR-100CREMA-DTiny ImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified