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 4751–4800 of 5044 papers

TitleStatusHype
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Contrastive Self-supervised Learning for Graph Classification—0
Dialogue-adaptive Language Model Pre-training From Quality EstimationCode0
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Self-Supervised Learning Based on Spatial Awareness for Medical Image AnalysisCode0
Puzzle-AE: Novelty Detection in Images through Solving PuzzlesCode0
Confidence-aware Adversarial Learning for Self-supervised Semantic MatchingCode0
Learning to Learn in a Semi-Supervised Fashion—0
Contrastive learning, multi-view redundancy, and linear models—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation—0
A Self-supervised GAN for Unsupervised Few-shot Object Recognition—0
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Variance-reduced Language Pretraining via a Mask Proposal Network—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
Exploring Relations in Untrimmed Videos for Self-Supervised Learning—0
Functional Regularization for Representation Learning: A Unified Theoretical PerspectiveCode0
Predicting What You Already Know Helps: Provable Self-Supervised Learning—0
MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
Learning Video Representations by Transforming Time—0
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation LearningCode0
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization—0
Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild—0
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic SegmentationCode0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling—0
Representation Learning with Video Deep InfoMax—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—0
Self-supervised Learning for Large-scale Item Recommendations—0
Self-Supervised Learning Across Domains—0
Instance-aware Self-supervised Learning for Nuclei Segmentation—0
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling—0
Video Representation Learning by Recognizing Temporal Transformations—0
CSLNSpeech: solving extended speech separation problem with the help of Chinese sign languageCode0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
An Open-World Simulated Environment for Developmental Robotics—0
Self-Supervised Learning of Context-Aware Pitch Prosody Representations—0
Improving Object Detection with Selective Self-supervised Self-training—0
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling—0
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation—0
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR VideosCode0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
Identifying Latent Stochastic Differential EquationsCode0
Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic WheelchairsCode0
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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