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 1751–1800 of 5044 papers

TitleStatusHype
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning StudyCode0
Particle Trajectory Representation Learning with Masked Point Modeling—0
Astromer 2—0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation—0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head Synthesis—0
Self-Supervised Learning Using Nonlinear Dependence—0
Language Bias in Self-Supervised Learning For Automatic Speech Recognition—0
A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment—0
Clustering Properties of Self-Supervised Learning—0
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling—0
Temperature-Free Loss Function for Contrastive Learning—0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT ClassificationCode0
Beyond-Labels: Advancing Open-Vocabulary Segmentation With Vision-Language Models—0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0—0
Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models—0
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?—0
Efficient Self-Supervised Grading of Prostate Cancer Pathology—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
Towards Intelligent Design: A Self-driven Framework for Collocated Clothing Synthesis Leveraging Fashion Styles and Textures—0
PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments—0
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition—0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
A Probabilistic Model for Self-Supervised Learning—0
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features—0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
With Great Backbones Comes Great Adversarial Transferability—0
A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications—0
Enhancing SAR Object Detection with Self-Supervised Pre-training on Masked Auto-Encoders—0
Disentangled Modeling of Preferences and Social Influence for Group RecommendationCode0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma PredictionCode0
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
InfoHier: Hierarchical Information Extraction via Encoding and Embedding—0
Applying General Turn-taking Models to Conversational Human-Robot Interaction—0
Pseudolabel guided pixels contrast for domain adaptive semantic segmentationCode0
Optimizing Speech Multi-View Feature Fusion through Conditional ComputationCode0
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision—0
Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis—0
Towards Early Prediction of Self-Supervised Speech Model Performance—0
Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics ProcessingCode0
Probing Speaker-specific Features in Speaker Representations—0
Learning Compact and Robust Representations for Anomaly Detection—0
Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach—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