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 1551–1600 of 5044 papers

TitleStatusHype
Investigating self-supervised features for expressive, multilingual voice conversion—0
Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework—0
Thoughts on Objectives of Sparse and Hierarchical Masked Image Model—0
Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions—0
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images—0
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference—0
Towards a Unified Representation Evaluation Framework Beyond Downstream TasksCode0
Score-based Self-supervised MRI Denoising—0
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection—0
Learning based convex approximation for constrained parametric optimization—0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems—0
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
Very High-Resolution Forest Mapping with TanDEM-X InSAR Data and Self-Supervised Learning—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
VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge InjectionCode0
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT ScansCode0
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
Hierarchical Context Learning of object components for unsupervised semantic segmentationCode0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
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
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
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
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
SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling—0
PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning—0
Can Masked Autoencoders Also Listen to Birds?—0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—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
H^3GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning—0
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions—0
An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open WorldCode0
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction—0
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data—0
Towards A Universal Graph Structural Encoder—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