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 1–50 of 5044 papers

TitleStatusHype
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys—0
Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder—0
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis—0
ShapeEmbed: a self-supervised learning framework for 2D contour quantification—0
World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World ModelCode0
RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation ModelsCode0
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing—0
Post-training for Deepfake Speech DetectionCode1
Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings—0
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer FeaturesCode0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models—0
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness—0
Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration—0
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis—0
CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment—0
Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control—0
Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI—0
Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised ModelsCode3
USAD: Universal Speech and Audio Representation via Distillation—0
SSAVSV: Towards Unified Model for Self-Supervised Audio-Visual Speaker Verification—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Probing for Phonology in Self-Supervised Speech Representations: A Case Study on Accent Perception—0
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots—0
Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching—0
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning—0
Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling—0
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning ModelsCode0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
Self-Supervised Enhancement for Depth from a Lightweight ToF Sensor with Monocular ImagesCode1
OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data—0
Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis—0
SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic SoundscapesCode2
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster DistillationCode1
LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN ArchitectureCode0
Attention, Please! Revisiting Attentive Probing for Masked Image ModelingCode1
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and PlanningCode7
ScaleLSD: Scalable Deep Line Segment Detection StreamlinedCode1
Urban1960SatSeg: Unsupervised Semantic Segmentation of Mid-20^th century Urban Landscapes with Satellite ImageriesCode2
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell DataCode1
Foundation Models in Medical Imaging -- A Review and Outlook—0
Employing self-supervised learning models for cross-linguistic child speech maturity classificationCode0
MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised LearningCode1
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Diffuse and Disperse: Image Generation with Representation Regularization—0
FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs—0
Circumventing Backdoor Space via Weight SymmetryCode0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—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