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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4851–4900 of 5044 papers

TitleStatusHype
When Does Self-supervision Improve Few-shot Learning?Code0
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic SurgeryCode0
Regularizing with Pseudo-Negatives for Continual Self-Supervised LearningCode0
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event DetectionCode0
Data-Efficient Sleep Staging with Synthetic Time Series PretrainingCode0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
Is Limited Participant Diversity Impeding EEG-based Machine Learning?Code0
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual RepresentationsCode0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
Is It a Plausible Colour? UCapsNet for Image ColourisationCode0
ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical ImagesCode0
Applying Unsupervised Semantic Segmentation to High-Resolution UAV Imagery for Enhanced Road Scene ParsingCode0
Data Efficient Contrastive Learning in Histopathology using Active SamplingCode0
A Large Encoder-Decoder Family of Foundation Models For Chemical LanguageCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph EmbeddingCode0
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social BehaviourCode0
Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability EstimationCode0
Investigate the Essence of Long-Tailed Recognition from a Unified PerspectiveCode0
Intra-video Positive Pairs in Self-Supervised Learning for UltrasoundCode0
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point CloudsCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time SeriesCode0
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision MakingCode0
Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy imagesCode0
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised LearningCode0
It is Never Too Late to Mend: Separate Learning for Multimedia RecommendationCode0
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image AnalysisCode0
Self-Supervised Metric Learning With Graph Clustering For Speaker DiarizationCode0
T3RD: Test-Time Training for Rumor Detection on Social MediaCode0
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point CloudCode0
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation LearningCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Inferring topological transitions in pattern-forming processes with self-supervised learningCode0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Inferring Actual Treatment Pathways from Patient RecordsCode0
Self-Supervised Monocular Depth HintsCode0
Inductive biases and Self Supervised Learning in modelling a physical heating systemCode0
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer FeaturesCode0
TADA: Training-free Attribution and Out-of-Domain Detection of Audio DeepfakesCode0
A Hierarchical Regression Chain Framework for Affective Vocal Burst RecognitionCode0
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and LocalizationCode0
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive LearningCode0
AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical KnowledgeCode0
Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion ClassificationCode0
Improving Self-supervised Learning with Automated Unsupervised Outlier ArbitrationCode0
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypesCode0
3D Human Pose Machines with Self-supervised LearningCode0
Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary ClassifierCode0
Show:102550
← PrevPage 98 of 101Next →
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