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 1551–1600 of 5044 papers

TitleStatusHype
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph LearningCode0
PSSL: Self-supervised Learning for Personalized Search with Contrastive SamplingCode0
A Large Encoder-Decoder Family of Foundation Models For Chemical LanguageCode0
Attention-based Contrastive Learning for Winograd SchemasCode0
Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclesCode0
Pseudolabel guided pixels contrast for domain adaptive semantic segmentationCode0
PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit SurfaceCode0
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning ModelsCode0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification MethodCode0
Continual Learning on Noisy Data Streams via Self-Purified ReplayCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point CloudsCode0
Puzzle-AE: Novelty Detection in Images through Solving PuzzlesCode0
Rethinking and Simplifying Bootstrapped Graph LatentsCode0
Continual Contrastive Learning for Image ClassificationCode0
AtmoDist: Self-supervised Representation Learning for Atmospheric DynamicsCode0
Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement LearningCode0
Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality RegularizationCode0
Contextual Molecule Representation Learning from Chemical Reaction KnowledgeCode0
Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised LearningCode0
Contextualized Structural Self-supervised Learning for Ontology MatchingCode0
Privacy-Preserving Models for Legal Natural Language ProcessingCode0
Contextualized Spatio-Temporal Contrastive Learning with Self-SupervisionCode0
Pretraining ECG Data with Adversarial Masking Improves Model Generalizability for Data-Scarce TasksCode0
Lightweight Cross-Modal Representation LearningCode0
Preserving Modality Structure Improves Multi-Modal LearningCode0
Pretext Tasks selection for multitask self-supervised speech representation learningCode0
PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation LearningCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation LearningCode0
Precision at Scale: Domain-Specific Datasets On-DemandCode0
Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained modelsCode0
Positive and negative sampling strategies for self-supervised learning on audio-video dataCode0
A Hierarchical Regression Chain Framework for Affective Vocal Burst RecognitionCode0
Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark DiscoveryCode0
POWN: Prototypical Open-World Node ClassificationCode0
Consistency is the key to further mitigating catastrophic forgetting in continual learningCode0
ActBERT: Learning Global-Local Video-Text RepresentationsCode0
Focus on the Positives: Self-Supervised Learning for Biodiversity MonitoringCode0
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio MaskingCode0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-IdentificationCode0
PixMIM: Rethinking Pixel Reconstruction in Masked Image ModelingCode0
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine LearningCode0
PixT3: Pixel-based Table-To-Text GenerationCode0
Conformal Credal Self-Supervised LearningCode0
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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