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 2251–2300 of 5044 papers

TitleStatusHype
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
Evolved Part Masking for Self-Supervised Learning—0
Evolutionary Augmentation Policy Optimization for Self-supervised Learning—0
A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More—0
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features—0
MCR-Data2vec 2.0: Improving Self-supervised Speech Pre-training via Model-level Consistency Regularization—0
Evolutionary algorithms meet self-supervised learning: a comprehensive survey—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
A self-supervised text-vision framework for automated brain abnormality detection—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels—0
EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera—0
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks—0
Event Camera Data Pre-training—0
Event Camera Data Dense Pre-training—0
Class-Discriminative Attention Maps for Vision Transformers—0
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement—0
Maximum Manifold Capacity Representations in State Representation Learning—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
CLAR: Contrastive Learning of Auditory Representations—0
Evaluating Visual Explanations of Attention Maps for Transformer-based Medical Imaging—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging—0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
Affine transformation estimation improves visual self-supervised learning—0
M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses—0
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals—0
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning—0
MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology—0
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction—0
Evaluating the fairness of fine-tuning strategies in self-supervised learning—0
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors—0
A Comprehensive Survey of Foundation Models in Medicine—0
Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact—0
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs—0
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data—0
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization—0
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups—0
MASR: Multi-label Aware Speech Representation—0
MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network—0
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training—0
Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation—0
Estimating Galactic Distances From Images Using Self-supervised Representation Learning—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Mask Hierarchical Features For Self-Supervised Learning—0
ESTAS: Effective and Stable Trojan Attacks in Self-supervised Encoders with One Target Unlabelled Sample—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning—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