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 1701–1750 of 5044 papers

TitleStatusHype
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning—0
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds—0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
Mixtraining: A Better Trade-Off Between Compute and Performance—0
Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models—0
Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives—0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting—0
TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-UltrasoundCode0
Distribution Matching for Self-Supervised Transfer LearningCode0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
Learning To Explore With Predictive World Model Via Self-Supervised Learning—0
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency—0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2—0
Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding—0
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution SelectionCode0
When do neural networks learn world models?—0
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models—0
On the Importance of Embedding Norms in Self-Supervised LearningCode0
Replay-free Online Continual Learning with Self-Supervised MultiPatchesCode0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia—0
SinSim: Sinkhorn-Regularized SimCLR—0
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—0
Screener: Self-supervised Pathology Segmentation Model for 3D Medical Images—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Large Cognition Model: Towards Pretrained EEG Foundation Model—0
Captured by Captions: On Memorization and its Mitigation in CLIP Models—0
Structure-preserving contrastive learning for spatial time seriesCode0
Motion Forecasting for Autonomous Vehicles: A Survey—0
SIGMA: Sheaf-Informed Geometric Multi-Agent PathfindingCode0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow—0
On the use of Performer and Agent Attention for Spoken Language Identification—0
Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation—0
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal imagesCode0
Learning Street View Representations with Spatiotemporal ContrastCode0
Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects—0
A Foundational Brain Dynamics Model via Stochastic Optimal Control—0
Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation EmbeddingsCode0
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images—0
Show:102550
← PrevPage 35 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