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 2301–2350 of 5044 papers

TitleStatusHype
FaceGPT: Self-supervised Learning to Chat about 3D Human Faces—0
Object-level Scene Deocclusion—0
Sustainable self-supervised learning for speech representations—0
Higher-Order Spatial Information for Self-Supervised Place Cell Learning—0
Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data—0
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge—0
NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks—0
ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations—0
Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation—0
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning—0
Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell ImagesCode0
On the social bias of speech self-supervised models—0
Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoring—0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks—0
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement—0
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning—0
SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade SensorsCode0
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders—0
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and BeyondCode0
Operational Latent SpacesCode0
Enhancing 2D Representation Learning with a 3D Prior—0
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation—0
Learning to Edit Visual Programs with Self-SupervisionCode0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR ModelsCode0
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic SurgeryCode0
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation—0
YODAS: Youtube-Oriented Dataset for Audio and Speech—0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Detecting Side Effects of Adverse Drug Reactions Through Drug-Drug Interactions Using Graph Neural Networks and Self-Supervised LearningCode0
Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation LearningCode0
Fill in the Gap! Combining Self-supervised Representation Learning with Neural Audio Synthesis for Speech Inpainting—0
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning—0
Relation Modeling and Distillation for Learning with Noisy Labels—0
Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing DataCode0
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model—0
Anomaly Detection by Context Contrasting—0
In-Context Symmetries: Self-Supervised Learning through Contextual World Models—0
Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation—0
SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation—0
Self-Supervised Learning Based Handwriting VerificationCode0
Spectral regularization for adversarially-robust representation learningCode0
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations—0
Transformer Meets Gated Residual Networks To Enhance Photoplethysmogram Artifact Detection Informed by Mutual Information Neural Estimation—0
Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning—0
Benchmarking Hierarchical Image Pyramid Transformer for the classification of colon biopsies and polyps in histopathology images—0
Masked Image Modelling for retinal OCT understandingCode0
Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual RepresentationsCode0
Capsule Network Projectors are Equivariant and Invariant LearnersCode0
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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