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 2551–2600 of 5044 papers

TitleStatusHype
Unsupervised learning based object detection using Contrastive Learning—0
OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets—0
Analysis of Using Sigmoid Loss for Contrastive Learning—0
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training—0
Target Speech Extraction with Pre-trained Self-supervised Learning Models—0
Probing Self-supervised Learning Models with Target Speech Extraction—0
"Understanding AI": Semantic Grounding in Large Language Models—0
Tracking Changing Probabilities via Dynamic LearnersCode0
Knowledge-guided EEG Representation Learning—0
GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic AssemblyCode0
Learning Low-Rank Feature for Thorax Disease Classification—0
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization—0
Scalable Graph Self-Supervised Learning—0
Advancing Human Action Recognition with Foundation Models trained on Unlabeled Public Videos—0
Affine transformation estimation improves visual self-supervised learning—0
Learning How To Ask: Cycle-Consistency Refines Prompts in Multimodal Foundation Models—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Mixtures of Experts Unlock Parameter Scaling for Deep RLCode0
UGMAE: A Unified Framework for Graph Masked Autoencoders—0
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation—0
Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers—0
Rethinking Graph Masked Autoencoders through Alignment and UniformityCode0
Persian Speech Emotion Recognition by Fine-Tuning Transformers—0
Low-Rank Approximation of Structural Redundancy for Self-Supervised LearningCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations—0
A self-supervised framework for learning whole slide representations—0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning—0
TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health RecordsCode0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts—0
VRMM: A Volumetric Relightable Morphable Head Model—0
Applying Unsupervised Semantic Segmentation to High-Resolution UAV Imagery for Enhanced Road Scene ParsingCode0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding—0
Positive and negative sampling strategies for self-supervised learning on audio-video dataCode0
Dual Lagrangian Learning for Conic Optimization—0
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation—0
Stereographic Spherical Sliced Wasserstein DistancesCode0
Exploring Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation—0
Deep Spectral Improvement for Unsupervised Image Instance SegmentationCode0
A Probabilistic Model Behind Self-Supervised LearningCode0
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge BasesCode0
SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning—0
Cooperative Knowledge Distillation: A Learner Agnostic ApproachCode0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and ClassificationCode0
Self-Supervised Contrastive Pre-Training for Multivariate Point Processes—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