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 2601–2650 of 5044 papers

TitleStatusHype
UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity—0
Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction—0
Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation—0
Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery—0
Uncertainty-Aware Self-supervised Neural Network for Liver T_1ρ Mapping with Relaxation Constraint—0
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review—0
"Understanding AI": Semantic Grounding in Large Language Models—0
Understanding and Improving the Role of Projection Head in Self-Supervised Learning—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning—0
Understanding Contrastive Learning Requires Incorporating Inductive Biases—0
Understanding Contrastive Learning Through the Lens of Margins—0
Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features—0
Understanding Masked Autoencoders From a Local Contrastive Perspective—0
Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation—0
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction—0
Understanding Self-supervised Learning via Information Bottleneck Principle—0
Understanding the limitations of self-supervised learning for tabular anomaly detection—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning—0
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions—0
Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos—0
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems—0
UniDoc: Unified Pretraining Framework for Document Understanding—0
Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects—0
Multimodal Molecular Pretraining via Modality Blending—0
Unified Pretraining Framework for Document Understanding—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
Self-Supervised Monocular 4D Scene Reconstruction for Egocentric Videos—0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset—0
Uniting contrastive and generative learning for event sequences models—0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—0
Universal Facial Encoding of Codec Avatars from VR Headsets—0
A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Universal Sound Separation with Self-Supervised Audio Masked Autoencoder—0
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training—0
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness—0
Unleashing the Power of Large Language Models for Group POI Recommendations—0
Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids—0
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning—0
Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoring—0
Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild—0
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—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