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 3951–4000 of 5044 papers

TitleStatusHype
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations—0
A Study of the Generalizability of Self-Supervised Representations—0
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer—0
Interest-oriented Universal User Representation via Contrastive Learning—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Unsupervised View-Invariant Human Posture Representation—0
Self-supervised learning methods and applications in medical imaging analysis: A surveyCode1
Self-Supervised Neural Architecture Search for Imbalanced DatasetsCode0
Self-supervised Contrastive Learning for EEG-based Sleep StagingCode1
The potential of self-supervised networks for random noise suppression in seismic data—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised LearningCode1
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism DetectionCode1
Deep Bregman Divergence for Contrastive Learning of Visual Representations—0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning—0
Self-Supervised Metric Learning With Graph Clustering For Speaker DiarizationCode0
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology—0
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation—0
WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need—0
Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers—0
Online Unsupervised Learning of Visual Representations and CategoriesCode0
TEASEL: A Transformer-Based Speech-Prefixed Language ModelCode1
Attention-based Contrastive Learning for Winograd SchemasCode0
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language ModelsCode0
Topic-Aware Contrastive Learning for Abstractive Dialogue SummarizationCode1
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow EstimationCode1
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks—0
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse ContextsCode1
Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-MixingCode0
Robot Localization and Navigation through Predictive Processing using LiDAR—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand HygieneCode0
Self-supervised Tumor Segmentation through Layer Decomposition—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyondCode0
Re-entry Prediction for Online Conversations via Self-Supervised LearningCode0
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption GenerationCode0
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region AlignmentCode0
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical ImagesCode1
Resource and data efficient self supervised learning—0
Motifs-based Recommender System via Hypergraph Convolution and Contrastive LearningCode0
Computer Vision Self-supervised Learning Methods on Time Series—0
Boosting Search Engines with Interactive Agents—0
Self-supervised Point Cloud Representation Learning via Separating Mixed ShapesCode1
EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera—0
ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasetsCode1
Learning to Discover Reflection Symmetry via Polar Matching Convolution—0
Digging into Uncertainty in Self-supervised Multi-view StereoCode1
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous DrivingCode1
Learning From Long-Tailed Data With Noisy Labels—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