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 1951–2000 of 5044 papers

TitleStatusHype
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology DiagnosticsCode0
MNN: Mixed Nearest-Neighbors for Self-Supervised LearningCode0
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic SegmentationCode0
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
Mixtures of Experts Unlock Parameter Scaling for Deep RLCode0
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent LabelingCode0
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local ComplementarityCode0
On the Out-of-Distribution Generalization of Self-Supervised LearningCode0
Replay-free Online Continual Learning with Self-Supervised MultiPatchesCode0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma PredictionCode0
MiniSUPERB: Lightweight Benchmark for Self-supervised Speech ModelsCode0
Mispronunciation detection using self-supervised speech representationsCode0
A Probabilistic Model Behind Self-Supervised LearningCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
BYEL : Bootstrap Your Emotion LatentCode0
MERTech: Instrument Playing Technique Detection Using Self-Supervised Pretrained Model With Multi-Task FinetuningCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded KnowledgeCode0
Adversarial Bootstrapped Question Representation Learning for Knowledge TracingCode0
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive LearningCode0
Membership Inference Attacks Against Self-supervised Speech ModelsCode0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
Memorization in Self-Supervised Learning Improves Downstream GeneralizationCode0
Metadata-guided Consistency Learning for High Content ImagesCode0
MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised RepresentationsCode0
Advancing Video Self-Supervised Learning via Image Foundation ModelsCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
Measuring the Robustness of Audio Deepfake DetectorsCode0
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised EmbeddingCode0
Dynamic Channel Selection in Self-Supervised LearningCode0
DyG2Vec: Efficient Representation Learning for Dynamic GraphsCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion TransformerCode0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
MAX: Masked Autoencoder for X-ray Fluorescence in Geological InvestigationCode0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation ModelsCode0
DualAug: Exploiting Additional Heavy Augmentation with OOD Data RejectionCode0
Masked Image Residual Learning for Scaling Deeper Vision TransformersCode0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
Masked Image Modeling Boosting Semi-Supervised Semantic SegmentationCode0
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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