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 4051–4100 of 5044 papers

TitleStatusHype
YODAS: Youtube-Oriented Dataset for Audio and Speech—0
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning—0
You Never Cluster Alone—0
Your head is there to move you around: Goal-driven models of the primate dorsal pathway—0
Zero-shot Active Learning Using Self Supervised Learning—0
Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection—0
Zero-Shot Generalization for Blockage Localization in mmWave Communication—0
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning—0
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model—0
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes—0
ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding—0
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation—0
Relation Modeling and Distillation for Learning with Noisy Labels—0
Relative Position Prediction as Pre-training for Text Encoders—0
Remixing-based Unsupervised Source Separation from Scratch—0
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training—0
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples—0
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images—0
Remote Sensing Scene Classification with Masked Image Modeling (MIM)—0
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation—0
Repoformer: Selective Retrieval for Repository-Level Code Completion—0
Representation Learning by Ranking under multiple tasks—0
Representation Learning for Wearable-Based Applications in the Case of Missing Data—0
Representation Learning of Geometric Trees—0
Representation Learning with Video Deep InfoMax—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
Representative & Fair Synthetic Data—0
Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Residual Energy-Based Models for End-to-End Speech Recognition—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Resource and data efficient self supervised learning—0
Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning—0
Response Selection for Multi-Party Conversations withDynamic Topic Tracking—0
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking—0
RestainNet: a self-supervised digital re-stainer for stain normalization—0
Results and findings of the 2021 Image Similarity Challenge—0
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning—0
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
Rethinking Self-Supervised Learning Within the Framework of Partial Information Decomposition—0
Rethinking supervised learning: insights from biological learning and from calling it by its name—0
Rethinking supervised learning: insights from biological learning and from calling it by its name—0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
Rethinking Weak-to-Strong Augmentation in Source-Free Domain Adaptive Object Detection—0
Retrieval-based Knowledge Augmented Vision Language Pre-training—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