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 3001–3050 of 5044 papers

TitleStatusHype
BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec—0
Self-Supervised Pretraining for 2D Medical Image SegmentationCode1
Supervised Contrastive Learning with Hard Negative SamplesCode1
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning—0
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds—0
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation—0
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and BeyondCode1
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
CounTR: Transformer-based Generalised Visual CountingCode1
SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Light-weight probing of unsupervised representations for Reinforcement LearningCode1
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning—0
Refine and Represent: Region-to-Object Representation LearningCode1
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis—0
Self-Supervised Endoscopic Image Key-Points MatchingCode1
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image SegmentationCode0
PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images—0
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue ClassificationCode0
Transfer Learning Application of Self-supervised Learning in ARPES—0
Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions—0
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles—0
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive LearningCode1
Heterogeneous Graph Masked AutoencodersCode1
Relational Self-Supervised Learning on GraphsCode1
Semantic-Enhanced Image Clustering—0
Generalised Co-Salient Object Detection—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
Test-time Training for Data-efficient UCDRCode0
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis—0
Siamese Prototypical Contrastive Learning—0
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical ProcessesCode1
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of SuccessCode0
Matching Multiple Perspectives for Efficient Representation Learning—0
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition—0
Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure AnalysisCode1
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification—0
Self-Supervised Vision Transformers for Malware DetectionCode1
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series ClassificationCode2
Enhancing Graph Contrastive Learning with Node Similarity—0
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems—0
Simulating Personal Food Consumption Patterns using a Modified Markov Chain—0
Contrastive Learning for Object DetectionCode0
CCRL: Contrastive Cell Representation LearningCode0
Show:102550
← PrevPage 61 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