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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2501–2550 of 5044 papers

TitleStatusHype
Out-of-distribution Partial Label Learning—0
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content RepresentationsCode0
On depth prediction for autonomous driving using self-supervised learning—0
Can Generative Models Improve Self-Supervised Representation Learning?Code0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis—0
JointMotion: Joint Self-Supervision for Joint Motion Prediction—0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification—0
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology—0
Lightweight Cross-Modal Representation LearningCode0
Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite imagesCode0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift—0
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
Pooling Image Datasets With Multiple Covariate Shift and Imbalance—0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
HeAR -- Health Acoustic Representations—0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
Self-Supervised Representation Learning with Meta Comprehensive Regularization—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
Rethinking The Uniformity Metric in Self-Supervised LearningCode0
Compact Speech Translation Models via Discrete Speech Units Pretraining—0
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors—0
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for GradientCode0
Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Video as the New Language for Real-World Decision Making—0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder—0
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning—0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
Text-guided HuBERT: Self-Supervised Speech Pre-training via Generative Adversarial Networks—0
DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning—0
Semi-supervised Counting via Pixel-by-pixel Density Distribution ModellingCode0
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image SegmentationCode0
A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound SegmentationCode0
The Common Stability Mechanism behind most Self-Supervised Learning ApproachesCode0
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off—0
Self-supervised Visualisation of Medical Image DatasetsCode0
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning—0
Multi-organ Self-supervised Contrastive Learning for Breast Lesion Segmentation—0
User-LLM: Efficient LLM Contextualization with User Embeddings—0
Contextual Molecule Representation Learning from Chemical Reaction KnowledgeCode0
Show:102550
← PrevPage 51 of 101Next →
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