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 3401–3450 of 5044 papers

TitleStatusHype
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos—0
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression—0
Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos—0
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells—0
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects—0
Self-Supervised Learning of Video-Induced Visual Invariances—0
Self-supervised learning of visual features through embedding images into text topic spaces—0
On the Role of Visual Cues in Audiovisual Speech Enhancement—0
VILLS -- Video-Image Learning to Learn Semantics for Person Re-Identification—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder—0
Self-Supervised Learning Using Nonlinear Dependence—0
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness—0
Self-Supervised Learning via multi-Transformation Classification for Action Recognition—0
Self-Supervised Learning via VICReg Enables Training of EMG Pattern Recognition Using Continuous Data with Unclear Labels—0
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?—0
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition—0
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Self-supervised Learning with Fully Convolutional Networks—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound—0
Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation—0
Self-supervised Learning with Local Attention-Aware Feature—0
Self-supervised Learning with Physics-aware Neural Networks I: Galaxy Model Fitting—0
Self-supervised Learning with Speech Modulation Dropout—0
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images—0
Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks—0
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization—0
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification—0
Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning—0
Self-Supervised Models of Speech Infer Universal Articulatory Kinematics—0
Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling—0
Self-supervised Motion Learning from Static Images—0
Self-Supervised Motion Retargeting with Safety Guarantee—0
Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition—0
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales—0
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification—0
Self-Supervised Multisensor Change Detection—0
Self-supervised Multi-view Clustering in Computer Vision: A Survey—0
Self-supervised Multi-view Disentanglement for Expansion of Visual Collections—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation—0
Self-supervised Neural Architecture Search—0
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling—0
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations—0
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation—0
Self-supervised Object Motion and Depth Estimation from Video—0
Self-Supervised Out-of-Distribution Detection in Brain CT Scans—0
Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR—0
Self-supervised Pre-training for Mirror Detection—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