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 2751–2800 of 5044 papers

TitleStatusHype
What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model—0
What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis—0
What Do We Maximize in Self-Supervised Learning?—0
What I See Is What You See: Joint Attention Learning for First and Third Person Video Co-analysis—0
What shapes the loss landscape of self-supervised learning?—0
What to align in multimodal contrastive learning?—0
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery—0
When Does Contrastive Visual Representation Learning Work?—0
When do neural networks learn world models?—0
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?—0
Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision—0
With Great Backbones Comes Great Adversarial Transferability—0
XAI for Self-supervised Clustering of Wireless Spectrum Activity—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
XLVIN: eXecuted Latent Value Iteration Nets—0
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
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning—0
10 Security and Privacy Problems in Large Foundation Models—0
Self-supervised audio representation learning for mobile devices—0
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction—0
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease—0
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks—0
Mobility-Aware Federated Self-supervised Learning in Vehicular Network—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond—0
Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW—0
Lossy Neural Compression for Geospatial Analytics: A Review—0
Maximizing Asynchronicity in Event-based Neural Networks—0
Fractal Graph Contrastive Learning—0
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport—0
A Survey of Generative Categories and Techniques in Multimodal Large Language Models—0
2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track—0
3D Cloud reconstruction through geospatially-aware Masked Autoencoders—0
3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI—0
3D Molecular Geometry Analysis with 2D 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