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 1951–2000 of 5044 papers

TitleStatusHype
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised LearningCode0
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology DiagnosticsCode0
Towards Scalable Foundation Models for Digital DermatologyCode0
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
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning—0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Learning predictable and robust neural representations by straightening image sequencesCode0
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 Information Criterion for Controlled Disentanglement of Multimodal DataCode0
An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions—0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
Revisiting MAE pre-training for 3D medical image segmentation—0
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning—0
Cross-Entropy Is All You Need To Invert the Data Generating Process—0
SimSiam Naming Game: A Unified Approach for Representation Learning and Emergent Communication—0
Multi-modal AI for comprehensive breast cancer prognostication—0
Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease—0
Accelerating Augmentation Invariance Pretraining—0
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models—0
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning—0
Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet B7 for Improved ClassificationCode0
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?—0
A contrastive-learning approach for auditory attention detection—0
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
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
Pseudo-label Refinement for Improving Self-Supervised Learning Systems—0
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup—0
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model—0
Normalizing self-supervised learning for provably reliable Change Point Detection—0
On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods—0
All models are wrong, some are useful: Model Selection with Limited LabelsCode0
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond—0
MAX: Masked Autoencoder for X-ray Fluorescence in Geological InvestigationCode0
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features—0
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series—0
MultiCamCows2024 -- A Multi-view Image Dataset for AI-driven Holstein-Friesian Cattle Re-Identification on a Working Farm—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Reducing Source-Private Bias in Extreme Universal Domain Adaptation—0
LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space—0
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning PerspectiveCode0
Learning to Customize Text-to-Image Diffusion In Diverse Context—0
EchoApex: A General-Purpose Vision Foundation Model for Echocardiography—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