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 4101–4150 of 5044 papers

TitleStatusHype
Semantic decoupled representation learning for remote sensing image change detection—0
StolenEncoder: Stealing Pre-trained Encoders in Self-supervised LearningCode0
STEdge: Self-training Edge Detection with Multi-layer Teaching and RegularizationCode0
SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data—0
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining—0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping—0
Reproducing BowNet: Learning Representations by Predicting Bags of Visual WordsCode0
Supervised Contrastive Learning for Recommendation—0
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning—0
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement—0
On the Effectiveness of Sampled Softmax Loss for Item Recommendation—0
Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning—0
Self-Supervised Approach to Addressing Zero-Shot Learning ProblemCode0
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough—0
Self-supervised Learning from 100 Million Medical Images—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior—0
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Align Representations With Base: A New Approach to Self-Supervised Learning—0
Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant LossCode0
Semantic-Aware Auto-Encoders for Self-Supervised Representation LearningCode0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning—0
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning—0
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Fine-grained Multi-Modal Self-Supervised Learning—0
Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach—0
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description ExtractionCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations—0
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation—0
Self-supervised clarification question generation for ambiguous multi-turn conversation—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Bayesian Graph Contrastive Learning—0
Improving Self-supervised Learning with Automated Unsupervised Outlier ArbitrationCode0
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications—0
Transferrable Contrastive Learning for Visual Domain Adaptation—0
Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker TrackingCode0
GEO-BLEU: Similarity Measure for Geospatial Sequences—0
Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
Concept Representation Learning with Contrastive Self-Supervised Learning—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