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 3951–4000 of 5044 papers

TitleStatusHype
Unsupervised learning based object detection using Contrastive Learning—0
Unsupervised Learning of Dense Visual Representations—0
Unsupervised Learning on a DIET: Datum IndEx as Target Free of Self-Supervision, Reconstruction, Projector Head—0
Unsupervised Machine Learning for Osteoporosis Diagnosis Using Singh Index Clustering on Hip Radiographs—0
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning—0
Self-Supervised Learning of Context-Aware Pitch Prosody Representations—0
Unsupervised Skill-Discovery and Skill-Learning in Minecraft—0
Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding—0
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications—0
Unsupervised Transfer Learning via Adversarial Contrastive Training—0
Unsupervised Transfer Learning with Self-Supervised Remedy—0
Unsupervised View-Invariant Human Posture Representation—0
Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)—0
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning—0
UPRec: User-Aware Pre-training for Recommender Systems—0
USAD: Universal Speech and Audio Representation via Distillation—0
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
User-LLM: Efficient LLM Contextualization with User Embeddings—0
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough—0
Using Navigational Information to Learn Visual Representations—0
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation—0
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video—0
Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model—0
Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata—0
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation—0
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations—0
Variance-Covariance Regularization Improves Representation Learning—0
Variance-reduced Language Pretraining via a Mask Proposal Network—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Variational Self-Supervised Contrastive Learning Using Beta Divergence—0
Variational Self-Supervised Learning—0
Vector-Symbolic Architecture for Event-Based Optical Flow—0
Very High-Resolution Forest Mapping with TanDEM-X InSAR Data and Self-Supervised Learning—0
VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging—0
Computer Vision Self-supervised Learning Methods on Time Series—0
Video as the New Language for Real-World Decision Making—0
Video-Foley: Two-Stage Video-To-Sound Generation via Temporal Event Condition For Foley Sound—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Video Representation Learning by Recognizing Temporal Transformations—0
Video Transformers: A Survey—0
Video Understanding as Machine Translation—0
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding—0
VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?—0
VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining—0
ViewMix: Augmentation for Robust Representation in Self-Supervised Learning—0
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation—0
Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
Vi-MIX FOR SELF-SUPERVISED VIDEO REPRESENTATION—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—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