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 2101–2150 of 5044 papers

TitleStatusHype
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain—0
Equivariance-based self-supervised learning for audio signal recovery from clipped measurements—0
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification—0
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs—0
Self-Supervised Learning for Identifying Defects in Sewer Footage—0
RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation LearningCode0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
Self-supervised learning for crystal property prediction via denoising—0
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers—0
SelectTTS: Synthesizing Anyone's Voice via Discrete Unit-Based Frame Selection—0
Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation—0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
EMP: Enhance Memory in Data Pruning—0
GSIFN: A Graph-Structured and Interlaced-Masked Multimodal Transformer-based Fusion Network for Multimodal Sentiment AnalysisCode0
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training—0
Self-supervised Speech Representations Still Struggle with African American Vernacular EnglishCode0
Disentangled Generative Graph Representation Learning—0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks—0
Symmetric masking strategy enhances the performance of Masked Image Modeling—0
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning—0
Self-supervised Learning for Geospatial AI: A Survey—0
Cell-ontology guided transcriptome foundation model—0
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features—0
SelfDRSC++: Self-Supervised Learning for Dual Reversed Rolling Shutter CorrectionCode0
Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound—0
PooDLe: Pooled and dense self-supervised learning from naturalistic videos—0
Speech Representation Learning Revisited: The Necessity of Separate Learnable Parameters and Robust Data Augmentation—0
CooPre: Cooperative Pretraining for V2X Cooperative Perception—0
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation—0
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis—0
Near, far: Patch-ordering enhances vision foundation models' scene understanding—0
kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speech—0
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?Code0
Uniting contrastive and generative learning for event sequences models—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Efficient onboard multi-task AI architecture based on self-supervised learning—0
Fine-gained air quality inference based on low-quality sensing data using self-supervised learning—0
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing—0
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition—0
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVCode0
Unsupervised Transfer Learning via Adversarial Contrastive Training—0
Convexity-based Pruning of Speech Representation Models—0
Representation Learning of Geometric Trees—0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
Exploring learning environments for label\-efficient cancer diagnosis—0
Whitening Consistently Improves Self-Supervised LearningCode0
LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language ModelsCode0
Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement—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