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 4701–4750 of 5044 papers

TitleStatusHype
Deep Metric Learning with Spherical Embedding—0
Detection of Maternal and Fetal Stress from the Electrocardiogram with Self-Supervised Representation LearningCode0
NICT Kyoto Submission for the WMT’20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation—0
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking—0
A Survey on Contrastive Self-supervised Learning—0
Self-supervised Representation Learning for Evolutionary Neural Architecture SearchCode0
Scene Flow from Point Clouds with or without Learning—0
PAL : Pretext-based Active Learning—0
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals—0
Multi-object tracking with self-supervised associating network—0
XLVIN: eXecuted Latent Value Iteration Nets—0
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration—0
Contrastive Learning with Adversarial Examples—0
Contrastive Self-Supervised Learning for Wireless Power ControlCode0
Self-Supervised Shadow Removal—0
Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion—0
Self-Supervised Learning of Part Mobility from Point Cloud SequenceCode0
SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks—0
CLAR: Contrastive Learning of Auditory Representations—0
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and BeyondCode0
From Local Structures to Size Generalization in Graph Neural Networks—0
On the surprising similarities between supervised and self-supervised models—0
For self-supervised learning, Rationality implies generalization, provablyCode0
Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive LearningCode0
Response Selection for Multi-Party Conversations withDynamic Topic Tracking—0
Self-Supervised Ranking for Representation Learning—0
Audio-Visual Self-Supervised Terrain Type Discovery for Mobile Platforms—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation—0
Are all negatives created equal in contrastive instance discrimination?—0
Rethinking supervised learning: insights from biological learning and from calling it by its name—0
Learning 3D Face Reconstruction with a Pose Guidance Network—0
A Human Ear Reconstruction Autoencoder—0
Pathological Visual Question Answering—0
Shot in the Dark: Few-Shot Learning with No Base-Class Labels—0
EqCo: Equivalent Rules for Self-supervised Contrastive LearningCode0
MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision—0
Learning Complete 3D Morphable Face Models from Images and Videos—0
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples—0
Understanding Self-supervised Learning with Dual Deep NetworksCode0
Adversarial Semi-Supervised Multi-Domain Tracking—0
On Size Generalization in Graph Neural Networks—0
Sense and Learn: Self-Supervision for Omnipresent Sensors—0
Clustering-based Unsupervised Generative Relation Extraction—0
Neural Identification for ControlCode0
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion—0
Conditional Hybrid GAN for Sequence Generation—0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
Learning to Identify Physical Parameters from Video Using Differentiable Physics—0
Show:102550
← PrevPage 95 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