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

Showing 19011950 of 5044 papers

TitleStatusHype
MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant FeaturesCode0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
Employing self-supervised learning models for cross-linguistic child speech maturity classificationCode0
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer InterfacesCode0
Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-MpoxCode0
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social BehaviourCode0
MortonNet: Self-Supervised Learning of Local Features in 3D Point CloudsCode0
Adversarial Momentum-Contrastive Pre-TrainingCode0
EMIT- Event-Based Masked Auto Encoding for Irregular Time SeriesCode0
Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and ClassificationCode0
EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding InspectionCode0
MOFO: MOtion FOcused Self-Supervision for Video UnderstandingCode0
MooseNet: A Trainable Metric for Synthesized Speech with a PLDA ModuleCode0
MSVQ: Self-Supervised Learning with Multiple Sample Views and QueuesCode0
Multi-modal Masked Siamese Network Improves Chest X-Ray Representation LearningCode0
Unsupervised Hyperspectral and Multispectral Image Fusion via Self-Supervised Modality DecouplingCode0
Modal-specific Pseudo Query Generation for Video Corpus Moment RetrievalCode0
Capsule Network Projectors are Equivariant and Invariant LearnersCode0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
MNN: Mixed Nearest-Neighbors for Self-Supervised LearningCode0
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent LabelingCode0
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?Code0
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic SegmentationCode0
Mixture of Self-Supervised LearningCode0
Mixtures of Experts Unlock Parameter Scaling for Deep RLCode0
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
Efficient Unsupervised Visual Representation Learning with Explicit Cluster BalancingCode0
Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?Code0
MixMask: Revisiting Masking Strategy for Siamese ConvNetsCode0
Modeling Emotions and Ethics with Large Language ModelsCode0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image SegmentationCode0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
Are Data-driven Explanations Robust against Out-of-distribution Data?Code0
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology DiagnosticsCode0
Mispronunciation detection using self-supervised speech representationsCode0
MiniSUPERB: Lightweight Benchmark for Self-supervised Speech ModelsCode0
Can Generative Models Improve Self-Supervised Representation Learning?Code0
Efficient Hierarchical Contrastive Self-supervising Learning for Time Series Classification via Importance-aware Resolution SelectionCode0
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative NormalizationCode0
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised RepresentationsCode0
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local ComplementarityCode0
Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker TrackingCode0
Metadata-guided Consistency Learning for High Content ImagesCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task FinetuningCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5Unverified
2Pretraining: ShEDImages & Text54.3Unverified
3Pretraining: e-MixImages & Text48.9Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7Unverified
2ResNet18Accuracy91.02Unverified
3MV-MRAccuracy89.67Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89Unverified
2ResNet18average top-1 classification accuracy92.58Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51Unverified
2ResNet18average top-1 classification accuracy69.31Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84Unverified
2ResNet18average top-1 classification accuracy51.67Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86Unverified