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 4801–4850 of 5044 papers

TitleStatusHype
Monocular Depth Estimation with Self-supervised Instance Adaptation—0
A Review on Deep Learning Techniques for Video Prediction—0
Models GenesisCode1
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis—0
Self-Supervised Monocular Scene Flow EstimationCode1
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
Towards Better Generalization: Joint Depth-Pose Learning without PoseNetCode1
Towards Lifelong Self-Supervision For Unpaired Image-to-Image TranslationCode0
COVID-CT-Dataset: A CT Scan Dataset about COVID-19Code1
Improving out-of-distribution generalization via multi-task self-supervised pretraining—0
Laplacian Denoising Autoencoder—0
Self-Supervised Learning for Domain Adaptation on Point-CloudsCode1
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow EstimationCode1
Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object RecognitionCode1
A Collective Learning Framework to Boost GNN Expressiveness—0
Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows—0
Temporally Coherent Embeddings for Self-Supervised Video Representation LearningCode1
Self-Supervised Contextual Bandits in Computer Vision—0
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels—0
Self-Supervised Log ParsingCode2
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural NetworksCode1
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning—0
On Compositions of Transformations in Contrastive Self-Supervised LearningCode1
Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach—0
Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a VideoCode0
A Survey on Deep Hashing Methods—0
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language ModelCode2
A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban EnvironmentCode0
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic NavigationCode1
Self-supervised Representation Learning for Ultrasound Video—0
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthCode2
Self-supervised Image Enhancement Network: Training with Low Light Images OnlyCode1
Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-trainingCode1
A Multi-view Perspective of Self-supervised Learning—0
Learning Dynamic Belief Graphs to Generalize on Text-Based GamesCode1
Disentangled Speech Embeddings using Cross-modal Self-supervision—0
CoLES: Contrastive Learning for Event Sequences with Self-SupervisionCode1
Self-supervised learning for audio-visual speaker diarization—0
Automatically Discovering and Learning New Visual Categories with Ranking StatisticsCode1
BADGR: An Autonomous Self-Supervised Learning-Based Navigation SystemCode1
A Simple Framework for Contrastive Learning of Visual RepresentationsCode2
Deep Self-Supervised Representation Learning for Free-Hand SketchCode1
Multi-task self-supervised learning for Robust Speech RecognitionCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
Self-supervised visual feature learning with curriculumCode0
Visually Guided Self Supervised Learning of Speech Representations—0
Semi-supervised Learning via Conditional Rotation Angle Estimation—0
CONSAC: Robust Multi-Model Fitting by Conditional Sample ConsensusCode1
Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy—0
Video Cloze Procedure for Self-Supervised Spatio-Temporal LearningCode1
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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