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 1101–1150 of 5044 papers

TitleStatusHype
Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids—0
Maximum Manifold Capacity Representations in State Representation Learning—0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
EchoSpike Predictive Plasticity: An Online Local Learning Rule for Spiking Neural Networks—0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding InspectionCode0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma—0
Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
SEL-CIE: Knowledge-Guided Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Images—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
Transcriptomics-guided Slide Representation Learning in Computational PathologyCode2
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Hi-GMAE: Hierarchical Graph Masked AutoencodersCode1
Selfsupervised learning for pathological speech detection—0
Beyond Traditional Single Object Tracking: A Survey—0
Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds—0
SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics—0
SARATR-X: Toward Building A Foundation Model for SAR Target RecognitionCode3
Investigating the 'Autoencoder Behavior' in Speech Self-Supervised Models: a focus on HuBERT's Pretraining—0
EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone TrainingCode3
Vector-Symbolic Architecture for Event-Based Optical Flow—0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition—0
T3RD: Test-Time Training for Rumor Detection on Social MediaCode0
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular DomainsCode1
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Machine Unlearning in Contrastive Learning—0
Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models—0
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare—0
MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning—0
Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection—0
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map ConstructionCode1
Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclesCode0
Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft—0
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting MaskCode2
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
The Entropy Enigma: Success and Failure of Entropy MinimizationCode2
EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised LearningCode0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
Open Implementation and Study of BEST-RQ for Speech Processing—0
Exploring Correlations of Self-Supervised Tasks for GraphsCode1
S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling—0
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data—0
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised LearningCode1
Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation—0
Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors—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