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 2001–2050 of 5044 papers

TitleStatusHype
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task FinetuningCode0
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic SurgeryCode0
Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-MixingCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
Metadata-guided Consistency Learning for High Content ImagesCode0
DDxT: Deep Generative Transformer Models for Differential DiagnosisCode0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Memorization in Self-Supervised Learning Improves Downstream GeneralizationCode0
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded KnowledgeCode0
Membership Inference Attacks Against Self-supervised Speech ModelsCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
DualAug: Exploiting Additional Heavy Augmentation with OOD Data RejectionCode0
Measuring the Robustness of Audio Deepfake DetectorsCode0
MedMAE: A Self-Supervised Backbone for Medical Imaging TasksCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVCode0
Past Movements-Guided Motion Representation Learning for Human Motion PredictionCode0
MAX: Masked Autoencoder for X-ray Fluorescence in Geological InvestigationCode0
Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan MirrorsCode0
Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream AssessmentCode0
Masked Image Residual Learning for Scaling Deeper Vision TransformersCode0
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation ModelsCode0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor NetworksCode0
Masked Image Modeling Boosting Semi-Supervised Semantic SegmentationCode0
Morphing Tokens Draw Strong Masked Image ModelsCode0
DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate AppraisalCode0
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye MovementsCode0
Masked Image Modelling for retinal OCT understandingCode0
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?Code0
DomCLP: Domain-wise Contrastive Learning with Prototype Mixup for Unsupervised Domain GeneralizationCode0
Masked Autoencoders are PDE LearnersCode0
Decoupling the Role of Data, Attention, and Losses in Multimodal TransformersCode0
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia ClassificationCode0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classificationCode0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasksCode0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systemsCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
A Novel Driver Distraction Behavior Detection Method Based on Self-supervised Learning with Masked Image ModelingCode0
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image SegmentationCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch BoostingCode0
A Novel Collaborative Self-Supervised Learning Method for Radiomic DataCode0
Manifold Contrastive Learning with Variational Lie Group OperatorsCode0
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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