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 2401–2450 of 5044 papers

TitleStatusHype
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative AnalysisCode0
Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin—0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning—0
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings—0
SemiPL: A Semi-supervised Method for Event Sound Source LocalizationCode0
Self-supervised learning for classifying paranasal anomalies in the maxillary sinusCode0
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation—0
Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces—0
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
Self-supervised visual learning in the low-data regime: a comparative evaluation—0
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries—0
Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan MirrorsCode0
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations—0
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control—0
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan—0
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data—0
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting—0
Self-Supervised Learning for User Localization—0
Hypergraph Self-supervised Learning with Sampling-efficient SignalsCode0
OPTiML: Dense Semantic Invariance Using Optimal Transport for Self-Supervised Medical Image Representation—0
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery—0
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition—0
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models—0
Pretraining Billion-scale Geospatial Foundational Models on Frontier—0
Deep Pattern Network for Click-Through Rate Prediction—0
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition—0
Modeling Emotions and Ethics with Large Language ModelsCode0
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?Code0
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
An Experimental Comparison Of Multi-view Self-supervised Methods For Music TaggingCode0
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature NoiseCode0
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling—0
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye MovementsCode0
Emerging Property of Masked Token for Effective Pre-training—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
Self-Supervised Learning of Color ConstancyCode0
Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling—0
How to Craft Backdoors with Unlabeled Data Alone?Code0
Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision—0
Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic TomographyCode0
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression—0
From Barlow Twins to Triplet Training: Differentiating Dementia with Limited DataCode0
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
HDR Imaging for Dynamic Scenes with Events—0
Multi-modal Learning for WebAssembly Reverse Engineering—0
Show:102550
← PrevPage 49 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