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 401–450 of 5044 papers

TitleStatusHype
A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications—0
With Great Backbones Comes Great Adversarial Transferability—0
A Survey of World Models for Autonomous DrivingCode1
Disentangled Modeling of Preferences and Social Influence for Group RecommendationCode0
A generalizable 3D framework and model for self-supervised learning in medical imagingCode2
Enhancing SAR Object Detection with Self-Supervised Pre-training on Masked Auto-Encoders—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma PredictionCode0
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
Scaling up self-supervised learning for improved surgical foundation modelsCode2
InfoHier: Hierarchical Information Extraction via Encoding and Embedding—0
Pseudolabel guided pixels contrast for domain adaptive semantic segmentationCode0
Applying General Turn-taking Models to Conversational Human-Robot Interaction—0
Optimizing Speech Multi-View Feature Fusion through Conditional ComputationCode0
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision—0
Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis—0
NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without ReferencesCode1
Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics ProcessingCode0
Towards Early Prediction of Self-Supervised Speech Model Performance—0
Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach—0
Probing Speaker-specific Features in Speaker Representations—0
Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and BenchmarksCode1
Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream AssessmentCode0
Learning Compact and Robust Representations for Anomaly Detection—0
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI DataCode0
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Towards a Generalizable Speech Marker for Parkinson's Disease Diagnosis—0
TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning—0
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions—0
Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation LearningCode0
Radar Signal Recognition through Self-Supervised Learning and Domain Adaptation—0
SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation—0
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised LearningCode0
Human Gaze Boosts Object-Centered Representation Learning—0
Seeing the Whole in the Parts in Self-Supervised Representation Learning—0
LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views—0
Gaussian Masked Autoencoders—0
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining—0
Enhancing Contrastive Learning for Retinal Imaging via Adjusted Augmentation Scales—0
Representation Learning of Lab Values via Masked AutoEncoderCode0
Self-Supervised Learning for Detecting AI-Generated Faces as AnomaliesCode0
3D Cloud reconstruction through geospatially-aware Masked Autoencoders—0
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud LearningCode0
MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector QuantizationCode3
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment—0
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature SpaceCode0
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects—0
RAEncoder: A Label-Free Reversible Adversarial Examples Encoder for Dataset Intellectual Property Protection—0
Show:102550
← PrevPage 9 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