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 4201–4250 of 5044 papers

TitleStatusHype
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study—0
Self-supervised Adversarial Robustness for the Low-label, High-data Regime—0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition—0
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis—0
Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging—0
Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks—0
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection—0
Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning—0
Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class DiscoveryCode0
Region-of-interest guided Supervoxel Inpainting for Self-supervisionCode0
Weakly- and Self-Supervised Learning for Content-Aware Deep Image RetargetingCode0
Relating Human Perception of Musicality to Prediction in a Predictive Coding ModelCode0
RegExplainer: Generating Explanations for Graph Neural Networks in Regression TasksCode0
Re-entry Prediction for Online Conversations via Self-Supervised LearningCode0
Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship AttributionCode0
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent SpaceCode0
Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing DataCode0
A Clinical Benchmark of Public Self-Supervised Pathology Foundation ModelsCode0
Random Teachers are Good TeachersCode0
A Probabilistic Model Behind Self-Supervised LearningCode0
Quantitative Imaging Principles Improves Medical Image LearningCode0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor NetworksCode0
MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of BehaviorCode0
SeLFVi: Self-Supervised Light-Field Video Reconstruction From Stereo VideoCode0
Bigger is not Always Better: The Effect of Context Size on Speech Pre-TrainingCode0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Wikipedia Edit Number Prediction based on Temporal Dynamics OnlyCode0
Replay-free Online Continual Learning with Self-Supervised MultiPatchesCode0
Verification-Aided Learning of Neural Network Barrier Functions with Termination GuaranteesCode0
Puzzle-AE: Novelty Detection in Images through Solving PuzzlesCode0
Graph Self-Supervised Learning for Endoscopic Image MatchingCode0
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning ModelsCode0
Semantic-Aware Auto-Encoders for Self-Supervised Representation LearningCode0
GraphFM: A Comprehensive Benchmark for Graph Foundation ModelCode0
Representation Learning of Lab Values via Masked AutoEncoderCode0
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised LearningCode0
Quantifying Representation Reliability in Self-Supervised Learning ModelsCode0
Understanding Masked Autoencoders via Hierarchical Latent Variable ModelsCode0
Understanding normalization in contrastive representation learning and out-of-distribution detectionCode0
PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit SurfaceCode0
PSSL: Self-supervised Learning for Personalized Search with Contrastive SamplingCode0
Reproducing BowNet: Learning Representations by Predicting Bags of Visual WordsCode0
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised LearningCode0
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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