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 601–650 of 5044 papers

TitleStatusHype
Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning—0
Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning—0
Understanding the Role of Equivariance in Self-supervised LearningCode0
Pattern Integration and Enhancement Vision Transformer for Self-Supervised Learning in Remote Sensing—0
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal AnalysisCode1
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology DiagnosticsCode0
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass—0
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised LearningCode0
Towards Scalable Foundation Models for Digital DermatologyCode0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
A Pre-training Framework that Encodes Noise Information for Speech Quality Assessment—0
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning—0
Learning predictable and robust neural representations by straightening image sequencesCode0
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning—0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Negative-Free Self-Supervised Gaussian Embedding of GraphsCode0
Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality RegularizationCode0
Identify Then Recommend: Towards Unsupervised Group RecommendationCode0
An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions—0
An Information Criterion for Controlled Disentanglement of Multimodal DataCode0
EchoFM: Foundation Model for Generalizable Echocardiogram AnalysisCode1
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
Revisiting MAE pre-training for 3D medical image segmentation—0
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control TasksCode2
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning—0
SimSiam Naming Game: A Unified Approach for Representation Learning and Emergent Communication—0
Cross-Entropy Is All You Need To Invert the Data Generating Process—0
Multi-modal AI for comprehensive breast cancer prognostication—0
Accelerating Augmentation Invariance Pretraining—0
Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease—0
PaPaGei: Open Foundation Models for Optical Physiological SignalsCode2
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models—0
Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet B7 for Improved ClassificationCode0
Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?—0
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning—0
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
A contrastive-learning approach for auditory attention detection—0
TabDPT: Scaling Tabular Foundation ModelsCode2
Self-Supervised Learning for Time Series: A Review & Critique of FITSCode0
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks—0
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images—0
ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical ImagesCode0
TIPS: Text-Image Pretraining with Spatial AwarenessCode2
A Multimodal Vision Foundation Model for Clinical DermatologyCode2
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
DM-Codec: Distilling Multimodal Representations for Speech TokenizationCode2
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup—0
Self-supervised contrastive learning performs non-linear system identificationCode1
Pseudo-label Refinement for Improving Self-Supervised Learning Systems—0
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model—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