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
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System—0
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis—0
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks—0
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling—0
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation—0
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery—0
Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation—0
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Leveraging the Third Dimension in Contrastive Learning—0
Leveraging Time Irreversibility with Order-Contrastive Pre-training—0
Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-visual Speech Recognition—0
Lifting Imbalanced Regression with Self-Supervised Learning—0
Lightweight feature encoder for wake-up word detection based on self-supervised speech representation—0
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models—0
Link Prediction with Contextualized Self-Supervision—0
LiRA: Learning Visual Speech Representations from Audio through Self-supervision—0
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing—0
LLaSA: Large Language and Structured Data Assistant—0
LLEDA -- Lifelong Self-Supervised Domain Adaptation—0
LLMcap: Large Language Model for Unsupervised PCAP Failure Detection—0
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction—0
Local Contrastive Feature learning for Tabular Data—0
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation—0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation—0
Localizing Memorization in SSL Vision Encoders—0
Locally Constrained Representations in Reinforcement Learning—0
Local Manifold Augmentation for Multiview Semantic Consistency—0
Local Policy Improvement for Recommender Systems—0
Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading—0
LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views—0
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation—0
Longitudinal Self-Supervision for COVID-19 Pathology Quantification—0
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration—0
Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach—0
Low-rank Optimal Transport: Approximation, Statistics and Debiasing—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Low-Resource Self-Supervised Learning with SSL-Enhanced TTS—0
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising—0
LPaintB: Learning to Paint from Self-Supervision—0
LSM-2: Learning from Incomplete Wearable Sensor Data—0
Longitudinal Self-Supervised Learning—0
LSTM Self-Supervision for Detailed Behavior Analysis—0
LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders—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