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 1501–1550 of 5044 papers

TitleStatusHype
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
Adapting Self-Supervised Learning for Computational Pathology—0
CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality—0
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication—0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition—0
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages—0
Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations—0
CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
Analysis of Using Sigmoid Loss for Contrastive Learning—0
Self-supervised audio representation learning for mobile devices—0
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning—0
Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations—0
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping—0
Cross-view and Cross-pose Completion for 3D Human Understanding—0
Hodge-Aware Contrastive Learning—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data—0
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection—0
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI—0
Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception—0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes—0
Autosen: improving automatic wifi human sensing through cross-modal autoencoder—0
Cross Pixel Optical Flow Similarity for Self-Supervised Learning—0
Autoregressive Sequence Modeling for 3D Medical Image Representation—0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Cross-modal Image Retrieval with Deep Mutual Information Maximization—0
Cross-Modal Discrete Representation Learning—0
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback—0
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers—0
HiVLP: Hierarchical Interactive Video-Language Pre-Training—0
HOME: High-Order Mixed-Moment-based Embedding for Representation Learning—0
Automatized Self-Supervised Learning for Skin Lesion Screening—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution—0
Cross-Entropy Is All You Need To Invert the Data Generating Process—0
Cross-domain Voice Activity Detection with Self-Supervised Representations—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning—0
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels—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