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 4451–4500 of 5044 papers

TitleStatusHype
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning—0
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring—0
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection—0
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images—0
Pathological Visual Question Answering—0
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks—0
PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization—0
Pattern Integration and Enhancement Vision Transformer for Self-Supervised Learning in Remote Sensing—0
PBVS 2024 Solution: Self-Supervised Learning and Sampling Strategies for SAR Classification in Extreme Long-Tail Distribution—0
PC-Adapter: Topology-Aware Adapter for Efficient Domain Adaption on Point Clouds with Rectified Pseudo-label—0
PEMS: Pre-trained Epidemic Time-series Models—0
Perceptual Group Tokenizer: Building Perception with Iterative Grouping—0
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect—0
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications—0
Persian Speech Emotion Recognition by Fine-Tuning Transformers—0
Personalization of Stress Mobile Sensing using Self-Supervised Learning—0
Personalized Food Image Classification: Benchmark Datasets and New Baseline—0
Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
Perturbation Ontology based Graph Attention Networks—0
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective—0
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation—0
Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning—0
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis—0
Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring—0
Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning—0
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks—0
Physics-Driven Local-Whole Elastic Deformation Modeling for Point Cloud Representation Learning—0
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning—0
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain—0
PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images—0
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition—0
PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning—0
Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer—0
Pixel-level Correspondence for Self-Supervised Learning from Video—0
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification—0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds—0
Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting—0
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection—0
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds—0
PointSmile: Point Self-supervised Learning via Curriculum Mutual Information—0
Ponder: Point Cloud Pre-training via Neural Rendering—0
PooDLe: Pooled and dense self-supervised learning from naturalistic videos—0
Pooling Image Datasets With Multiple Covariate Shift and Imbalance—0
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images—0
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution—0
Predicting What You Already Know Helps: Provable Self-Supervised Learning—0
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation—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