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 4401–4450 of 5044 papers

TitleStatusHype
Compound Figure Separation of Biomedical Images with Side LossCode0
Reasoning-Modulated Representations—0
Unsupervised Skill-Discovery and Skill-Learning in Minecraft—0
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study—0
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D CavitiesCode0
Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing ImagesCode0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset—0
Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation—0
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation—0
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation—0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning—0
Investigate the Essence of Long-Tailed Recognition from a Unified PerspectiveCode0
Multi-Level Graph Contrastive Learning—0
InfoNCE is variational inference in a recognition parameterised model—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Continual Contrastive Learning for Image ClassificationCode0
Pretext Tasks selection for multitask self-supervised speech representation learningCode0
Leveraging Hidden Structure in Self-Supervised Learning—0
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learningCode0
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images—0
Intent Disentanglement and Feature Self-supervision for Novel Recommendation—0
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning—0
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature—0
Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis—0
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Credal Self-Supervised LearningCode0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving—0
Trainable Class Prototypes for Few-Shot Learning—0
Coherent, super resolved radar beamforming using self-supervised learning—0
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism—0
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images—0
Spatio-temporal Contrastive Domain Adaptation for Action Recognition—0
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification—0
Partial Person Re-Identification With Part-Part Correspondence Learning—0
Prototype Augmentation and Self-Supervision for Incremental Learning—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Novelty Detection via Contrastive Learning with Negative Data Augmentation—0
LiRA: Learning Visual Speech Representations from Audio through Self-supervision—0
Watching Too Much Television is Good: Self-Supervised Audio-Visual Representation Learning from Movies and TV Shows—0
Nonequilibrium thermodynamics of self-supervised learning—0
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction—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