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 751–800 of 5044 papers

TitleStatusHype
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion—0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
FGR-Net:Interpretable fundus imagegradeability classification based on deepreconstruction learning—0
A Simple HMM with Self-Supervised Representations for Phone Segmentation—0
Self-supervised Learning for Acoustic Few-Shot Classification—0
Informative Subgraphs Aware Masked Auto-Encoder in Dynamic GraphsCode1
Leveraging Self-Supervised Learning for Speaker DiarizationCode3
The T05 System for The VoiceMOS Challenge 2024: Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic SpeechCode3
On the Generalizability of Foundation Models for Crop Type MappingCode0
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling—0
Exploring SSL Discrete Tokens for Multilingual ASR—0
Exploring SSL Discrete Speech Features for Zipformer-based Contextual ASRCode0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
Exploiting Supervised Poison Vulnerability to Strengthen Self-Supervised DefenseCode0
Autoregressive Sequence Modeling for 3D Medical Image Representation—0
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training—0
Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing—0
Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space ModelsCode1
Self-Supervised Learning of Iterative Solvers for Constrained Optimization—0
Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation—0
What to align in multimodal contrastive learning?—0
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point CloudsCode0
Data Collection-free Masked Video Modeling—0
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat ImageryCode0
Label-free Monitoring of Self-Supervised Learning Progress—0
DetailCLIP: Detail-Oriented CLIP for Fine-Grained TasksCode2
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget—0
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
SS-BRPE: Self-Supervised Blind Room Parameter Estimation Using Attention MechanismsCode0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Audio-Guided Fusion Techniques for Multimodal Emotion Analysis—0
A Survey on Mixup Augmentations and BeyondCode2
Explicit Mutual Information Maximization for Self-Supervised Learning—0
UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity—0
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain—0
An Analysis of Linear Complexity Attention Substitutes with BEST-RQ—0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—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
UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk EstimateCode1
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs—0
Self-Supervised Learning for Identifying Defects in Sewer Footage—0
MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec TransformerCode9
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
SelectTTS: Synthesizing Anyone's Voice via Discrete Unit-Based Frame Selection—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