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 3901–3950 of 5044 papers

TitleStatusHype
Federated Self-supervised Learning for Heterogeneous Clients—0
Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning—0
Low-rank Optimal Transport: Approximation, Statistics and Debiasing—0
SCVRL: Shuffled Contrastive Video Representation Learning—0
Multi-Augmentation for Efficient Visual Representation Learning for Self-supervised Pre-trainingCode0
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation—0
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods—0
Learning Dense Reward with Temporal Variant Self-Supervision—0
Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation—0
Deploying self-supervised learning in the wild for hybrid automatic speech recognition—0
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer—0
On the Difficulty of Defending Self-Supervised Learning against Model ExtractionCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers—0
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning—0
Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations—0
Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimationCode0
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With TransformersCode0
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning—0
Silence is Sweeter Than Speech: Self-Supervised Model Using Silence to Store Speaker Information—0
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising—0
IMU Based Deep Stride Length Estimation With Self-Supervised Learning—0
The NT-Xent loss upper bound—0
Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition—0
Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model—0
On Negative Sampling for Audio-Visual Contrastive Learning from Movies—0
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?—0
Label Anchored Contrastive Learning for Language Understanding—0
Masked Image Modeling Advances 3D Medical Image Analysis—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Unified Pretraining Framework for Document Understanding—0
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain ImagesCode0
Time-based Self-supervised Learning for Wireless Capsule Endoscopy—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