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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4451–4500 of 5044 papers

TitleStatusHype
Revisiting Model Stitching to Compare Neural Representations—0
Pre-Trained Models: Past, Present and Future—0
SAS: Self-Augmentation Strategy for Language Model Pre-trainingCode0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection—0
Cross-Modal Discrete Representation Learning—0
MST: Masked Self-Supervised Transformer for Visual Representation—0
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition—0
Cross-domain Contrastive Learning for Unsupervised Domain AdaptationCode0
Self-supervision of Feature Transformation for Further Improving Supervised Learning—0
Self-supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning—0
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)—0
Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation—0
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision MakingCode0
Incremental False Negative Detection for Contrastive Learning—0
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review—0
Integrating Auxiliary Information in Self-supervised Learning—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
Self-supervised Dialogue Learning for Spoken Conversational Question Answering—0
Self-Supervised Learning of Domain Invariant Features for Depth Estimation—0
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes—0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—0
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks—0
You Never Cluster Alone—0
OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots—0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis—0
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task—0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning—0
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning—0
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation—0
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms—0
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data AnnotationsCode0
Encoders and Ensembles for Task-Free Continual Learning—0
Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models—0
GraphVICRegHSIC: Towards improved self-supervised representation learning for graphs with a hyrbid loss functionCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds—0
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk—0
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement—0
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
Semi-supervised Contrastive Learning with Similarity Co-calibration—0
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging—0
Semi-supervised Learning for Identifying the Likelihood of Agitation in People with DementiaCode0
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation—0
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation—0
When Does Contrastive Visual Representation Learning Work?—0
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction—0
Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection—0
Show:102550
← PrevPage 90 of 101Next →
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