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 151–200 of 5044 papers

TitleStatusHype
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach—0
Very High-Resolution Forest Mapping with TanDEM-X InSAR Data and Self-Supervised Learning—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data—0
VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge InjectionCode0
A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT ScansCode0
SpectrumFM: A Foundation Model for Intelligent Spectrum ManagementCode1
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning—0
Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark StudyCode0
Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling—0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
Hierarchical Context Learning of object components for unsupervised semantic segmentationCode0
Contextures: The Mechanism of Representation Learning—0
Supervised Pretraining for Material Property Prediction—0
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Full waveform inversion with CNN-based velocity representation extension—0
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction—0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
Automated Measurement of Eczema Severity with Self-Supervised Learning—0
On feature representations for marmoset vocal communication analysis—0
Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection—0
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-AttentionCode1
Can We Ignore Labels In Out of Distribution Detection?—0
HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis—0
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3DCode3
PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning—0
SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling—0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—0
Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code SelectionCode1
Can Masked Autoencoders Also Listen to Birds?—0
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic DecompositionCode0
RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning—0
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction—0
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions—0
H^3GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning—0
An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open WorldCode0
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic ImagesCode0
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data—0
Towards A Universal Graph Structural Encoder—0
Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning—0
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals—0
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System—0
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
Learning Object Focused Attention—0
Impact of Language Guidance: A Reproducibility Study—0
Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography DatabasesCode1
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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