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 1501–1550 of 5044 papers

TitleStatusHype
Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM—0
A Survey of Generative Categories and Techniques in Multimodal Large Language Models—0
Graph Positional Autoencoders as Self-supervised Learners—0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis—0
ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding—0
Supervised and self-supervised land-cover segmentation & classification of the Biesbosch wetlands—0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Automated data curation for self-supervised learning in underwater acoustic analysis—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning—0
Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey—0
Training Articulatory Inversion Models for Interspeaker Consistency—0
WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification—0
Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation—0
Advancing Video Self-Supervised Learning via Image Foundation ModelsCode0
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image SegmentationCode0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction—0
Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain—0
VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining—0
Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis—0
On the Out-of-Distribution Generalization of Self-Supervised LearningCode0
Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models—0
Investigating Fine- and Coarse-grained Structural Correspondences Between Deep Neural Networks and Human Object Image Similarity Judgments Using Unsupervised Alignment—0
Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT ImagesCode0
VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging—0
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology—0
Detection of Underwater Multi-Targets Based on Self-Supervised Learning and Deformable Path Aggregation Feature Pyramid Network—0
Physics-Driven Local-Whole Elastic Deformation Modeling for Point Cloud Representation Learning—0
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification—0
Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment—0
Self-Supervised Learning for Image Segmentation: A Comprehensive Survey—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings—0
3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy—0
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics—0
Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning—0
Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data CurriculumCode0
PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation LearningCode0
Exploring the Potential of SSL Models for Sound Event Detection—0
Toward Adaptive Categories: Dimensional Governance for Agentic AI—0
Fractal Graph Contrastive Learning—0
Maximizing Asynchronicity in Event-based Neural Networks—0
PhiNet v2: A Mask-Free Brain-Inspired Vision Foundation Model from VideoCode0
Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning—0
GAIA: A Foundation Model for Operational Atmospheric Dynamics—0
A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware CapabilityCode0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
BioVFM-21M: Benchmarking and Scaling Self-Supervised Vision Foundation Models for Biomedical Image AnalysisCode0
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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