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 2451–2500 of 5044 papers

TitleStatusHype
Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation—0
Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models—0
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Automatized Self-Supervised Learning for Skin Lesion Screening—0
Hyperspherically Regularized Networks for Self-Supervision—0
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
Cross-Entropy Is All You Need To Invert the Data Generating Process—0
HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection—0
Cross-domain Voice Activity Detection with Self-Supervised Representations—0
Hyperbolic Contrastive Learning—0
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels—0
Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction—0
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference—0
Hybrid Interest Modeling for Long-tailed Users—0
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning—0
Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings—0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—0
Cross-domain few-shot learning with unlabelled data—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
Automatic Equalization for Individual Instrument Tracks Using Convolutional Neural Networks—0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
Human Gaze Boosts Object-Centered Representation Learning—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation—0
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors—0
Cross-BERT for Point Cloud Pretraining—0
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?—0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels—0
A Multi-view Perspective of Self-supervised Learning—0
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics—0
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects—0
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?—0
How to Understand Masked Autoencoders—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
How to Scale Your EMA—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations—0
How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning—0
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario—0
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?—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