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 1451–1500 of 5044 papers

TitleStatusHype
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis—0
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness—0
The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models—0
Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control—0
Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI—0
USAD: Universal Speech and Audio Representation via Distillation—0
SSAVSV: Towards Unified Model for Self-Supervised Audio-Visual Speaker Verification—0
Probing for Phonology in Self-Supervised Speech Representations: A Case Study on Accent Perception—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots—0
Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching—0
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning—0
Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling—0
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning ModelsCode0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data—0
Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis—0
LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN ArchitectureCode0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs—0
Foundation Models in Medical Imaging -- A Review and Outlook—0
Employing self-supervised learning models for cross-linguistic child speech maturity classificationCode0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Diffuse and Disperse: Image Generation with Representation Regularization—0
Circumventing Backdoor Space via Weight SymmetryCode0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
IMPA-HGAE:Intra-Meta-Path Augmented Heterogeneous Graph Autoencoder—0
TADA: Training-free Attribution and Out-of-Domain Detection of Audio DeepfakesCode0
When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive LearningCode0
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning—0
Rethinking Contrastive Learning in Session-based RecommendationCode0
LSM-2: Learning from Incomplete Wearable Sensor Data—0
Tone recognition in low-resource languages of North-East India: peeling the layers of SSL-based speech models—0
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport—0
Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning—0
Synthetic Speech Source Tracing using Metric Learning—0
Sounding Like a Winner? Prosodic Differences in Post-Match Interviews—0
MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements—0
HASRD: Hierarchical Acoustic and Semantic Representation Disentanglement—0
Self-Supervised-ISAR-Net Enables Fast Sparse ISAR Imaging—0
PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition—0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
Towards Unified Neural Decoding with Brain Functional Network Modeling—0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded KnowledgeCode0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR—0
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction—0
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization—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