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 4551–4600 of 5044 papers

TitleStatusHype
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised LearningCode0
Enhance the Visual Representation via Discrete Adversarial TrainingCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic WheelchairsCode0
MixMask: Revisiting Masking Strategy for Siamese ConvNetsCode0
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data AnnotationsCode0
CLAWSAT: Towards Both Robust and Accurate Code ModelsCode0
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised RepresentationsCode0
Past Movements-Guided Motion Representation Learning for Human Motion PredictionCode0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Enhanced Masked Image Modeling for Analysis of Dental Panoramic RadiographsCode0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
Mispronunciation detection using self-supervised speech representationsCode0
MiniSUPERB: Lightweight Benchmark for Self-supervised Speech ModelsCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionCode0
CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield ImagesCode0
Empower Nested Boolean Logic via Self-Supervised Curriculum LearningCode0
Employing self-supervised learning models for cross-linguistic child speech maturity classificationCode0
EMIT- Event-Based Masked Auto Encoding for Irregular Time SeriesCode0
EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding InspectionCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
BAL: Balancing Diversity and Novelty for Active LearningCode0
Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention DiscriminatorCode0
Metadata-guided Consistency Learning for High Content ImagesCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task FinetuningCode0
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)Code0
Efficient Unsupervised Visual Representation Learning with Explicit Cluster BalancingCode0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
Memorization in Self-Supervised Learning Improves Downstream GeneralizationCode0
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image SegmentationCode0
Membership Inference Attacks Against Self-supervised Speech ModelsCode0
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded KnowledgeCode0
Towards Adversarial Robustness And Backdoor Mitigation in SSLCode0
MedMAE: A Self-Supervised Backbone for Medical Imaging TasksCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Spectral regularization for adversarially-robust representation learningCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology DiagnosticsCode0
Measuring the Robustness of Audio Deepfake DetectorsCode0
MAX: Masked Autoencoder for X-ray Fluorescence in Geological InvestigationCode0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
Spectrograms Are Sequences of PatchesCode0
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation ModelsCode0
Self-Supervised Image Prior Learning With GMM From a Single Noisy ImageCode0
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution SelectionCode0
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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