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 2401–2450 of 5044 papers

TitleStatusHype
Improving Accented Speech Recognition with Multi-Domain Training—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers—0
Improvements to context based self-supervised learning—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations—0
Analysis of Using Sigmoid Loss for Contrastive Learning—0
Adapting Self-Supervised Learning for Computational Pathology—0
Abnormality-Driven Representation Learning for Radiology Imaging—0
3D Cloud reconstruction through geospatially-aware Masked Autoencoders—0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit—0
Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach—0
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning—0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning—0
Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching—0
Improved Cross-Lingual Transfer Learning For Automatic Speech Translation—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
Improved baselines for vision-language pre-training—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
Imposing Consistency for Optical Flow Estimation—0
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data—0
Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view—0
IMPA-HGAE:Intra-Meta-Path Augmented Heterogeneous Graph Autoencoder—0
Impact of Language Guidance: A Reproducibility Study—0
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI—0
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection—0
Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations—0
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding—0
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications—0
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations—0
Imaging with Equivariant Deep Learning—0
Autosen: improving automatic wifi human sensing through cross-modal autoencoder—0
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
Image Compression with Product Quantized Masked Image Modeling—0
Cross Pixel Optical Flow Similarity for Self-Supervised Learning—0
Autoregressive Sequence Modeling for 3D Medical Image Representation—0
Image Coding for Machines with Omnipotent Feature Learning—0
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms—0
Cross-modal Image Retrieval with Deep Mutual Information Maximization—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Cross-Modal Discrete Representation Learning—0
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback—0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception—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