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 1701–1750 of 5044 papers

TitleStatusHype
Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite ImageryCode0
Open-source framework for detecting bias and overfitting for large pathology imagesCode0
Operational Latent SpacesCode0
On the Role of Discrete Tokenization in Visual Representation LearningCode0
On the Stepwise Nature of Self-Supervised LearningCode0
Extracting speaker and emotion information from self-supervised speech models via channel-wise correlationsCode0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised LearningCode0
On the Importance of Embedding Norms in Self-Supervised LearningCode0
On the Out-of-Distribution Generalization of Self-Supervised LearningCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
On the Generalization and Causal Explanation in Self-Supervised LearningCode0
On the Transferability of Visual Features in Generalized Zero-Shot LearningCode0
Collaborative Auto-encoding for Blind Image Quality AssessmentCode0
On the Difficulty of Defending Self-Supervised Learning against Model ExtractionCode0
Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learningCode0
Online Unsupervised Learning of Visual Representations and CategoriesCode0
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-LanguageCode0
Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic LabelsCode0
Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing ImagesCode0
Object discovery and representation networksCode0
Exploring SSL Discrete Speech Features for Zipformer-based Contextual ASRCode0
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative AnalysisCode0
Exploring Self-Supervised Representation Learning For Low-Resource Medical Image AnalysisCode0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet B7 for Improved ClassificationCode0
Novel Class Discovery: an Introduction and Key ConceptsCode0
AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical KnowledgeCode0
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical LearningCode0
On the Generalizability of Foundation Models for Crop Type MappingCode0
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic SegmentationCode0
Exploring Green AI for Audio Deepfake DetectionCode0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
Exploring Expression-related Self-supervised Learning for Affective Behaviour AnalysisCode0
Noise-Robust Keyword Spotting through Self-supervised PretrainingCode0
Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth EstimationCode0
Neural Koopman prior for data assimilationCode0
Self-Supervised Approach to Addressing Zero-Shot Learning ProblemCode0
A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound SegmentationCode0
Do Invariances in Deep Neural Networks Align with Human Perception?Code0
Explored An Effective Methodology for Fine-Grained Snake RecognitionCode0
Neural Identification for ControlCode0
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated NoiseCode0
Self-supervised network distillation: an effective approach to exploration in sparse reward environmentsCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Exploiting Supervised Poison Vulnerability to Strengthen Self-Supervised DefenseCode0
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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