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 951–1000 of 5044 papers

TitleStatusHype
Heterogeneous Hypergraph Embedding for Recommendation SystemsCode0
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual FeaturesCode0
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition—0
LLMcap: Large Language Model for Unsupervised PCAP Failure Detection—0
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks—0
SA-WavLM: Speaker-Aware Self-Supervised Pre-training for Mixture Speech—0
VAE-based Phoneme Alignment Using Gradient Annealing and SSL Acoustic FeaturesCode1
Precision at Scale: Domain-Specific Datasets On-DemandCode0
A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production—0
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge—0
Towards the Next Frontier in Speech Representation Learning Using Disentanglement—0
Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry—0
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked PretrainingCode0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTsCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
Towards Robust Speech Representation Learning for Thousands of Languages—0
Diffusion Models and Representation Learning: A SurveyCode2
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views—0
Multi-modal Food Recommendation using Clustering and Self-supervised Learning—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million imagesCode0
Mixture of Experts in a Mixture of RL settings—0
Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention DiscriminatorCode0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation—0
Self-Supervised Embeddings for Detecting Individual Symptoms of Depression—0
Masked Generative Extractor for Synergistic Representation and 3D Generation of Point Clouds—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
AND: Audio Network Dissection for Interpreting Deep Acoustic Models—0
BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals—0
Cross-domain Transfer of Valence Preferences via a Meta-optimization ApproachCode0
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement LearningCode0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
The Hidden Pitfalls of the Cosine Similarity Loss—0
UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation—0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
Speech Analysis of Language Varieties in ItalyCode0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease DiagnosisCode0
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning—0
ViLCo-Bench: VIdeo Language COntinual learning BenchmarkCode1
Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing—0
Scale-Translation Equivariant Network for Oceanic Internal Solitary Wave LocalizationCode0
Semantic Graph Consistency: Going Beyond Patches for Regularizing Self-Supervised Vision Transformers—0
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised RepresentationsCode0
Deep self-supervised learning with visualisation for automatic gesture recognitionCode0
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
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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