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 1851–1900 of 5044 papers

TitleStatusHype
How to Scale Your EMA—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
How to Understand Masked Autoencoders—0
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?—0
Knowledge-guided EEG Representation Learning—0
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology—0
Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition—0
From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling—0
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Human Gaze Boosts Object-Centered Representation Learning—0
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—0
Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings—0
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures—0
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference—0
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis—0
From Chaos to Clarity: 3DGS in the Dark—0
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision—0
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
Hyperbolic Contrastive Learning—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Cross-domain Voice Activity Detection with Self-Supervised Representations—0
HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Hyperspherically Regularized Networks for Self-Supervision—0
Friends and Foes in Learning from Noisy Labels—0
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models—0
Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Knowledge distillation from language model to acoustic model: a hierarchical multi-task learning approach—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts—0
Constrained Mean Shift for Representation Learning—0
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Image Coding for Machines with Omnipotent Feature Learning—0
Image Compression with Product Quantized Masked Image Modeling—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance—0
Frequency-Aware Self-Supervised Long-Tailed Learning—0
A survey on Self Supervised learning approaches for improving Multimodal representation learning—0
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—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