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 301–350 of 5044 papers

TitleStatusHype
Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation—0
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and RetentionCode1
SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, UpdatedCode1
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event DetectionCode0
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction—0
Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar—0
CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers—0
Your contrastive learning problem is secretly a distribution alignment problemCode1
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds—0
SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning—0
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
Mixtraining: A Better Trade-Off Between Compute and Performance—0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models—0
Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised LearningCode1
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
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
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information BottleneckCode1
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning FrameworkCode1
Learning To Explore With Predictive World Model Via Self-Supervised Learning—0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency—0
Masked Latent Prediction and Classification for Self-Supervised Audio Representation LearningCode1
Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language ModelCode1
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
SinSim: Sinkhorn-Regularized SimCLR—0
On the Importance of Embedding Norms in Self-Supervised LearningCode0
Replay-free Online Continual Learning with Self-Supervised MultiPatchesCode0
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia—0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models—0
When do neural networks learn world models?—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
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Large Cognition Model: Towards Pretrained EEG Foundation Model—0
Captured by Captions: On Memorization and its Mitigation in CLIP Models—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Motion Forecasting for Autonomous Vehicles: A Survey—0
Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow—0
Structure-preserving contrastive learning for spatial time seriesCode0
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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