Latest Articles for Deep Learning

Innovative use of LUTs enhances FPGA performance for deep learning tasks.

Exploring the integration of deep learning with Bayesian methods to improve data estimation.

This article compares U-Net and transformer-based models for microscopy image segmentation.

MambaJSCC integrates coding techniques for better wireless image communication.

A new method improves computer model efficiency while maintaining performance.

Introducing MG-Net, a framework to improve quantum optimization techniques through deep learning.

A new method enhances detection accuracy in medical imaging.

This study focuses on enhancing image quality through RAW data restoration methods.

New method enhances image matching from various camera spectra.

This article discusses improving computer predictions in videos by straightening image sequences.

Explore local learning methods transforming neural network training.

Introducing a method to enhance privacy without sacrificing model accuracy.

Exploring a fresh approach to improve semantic segmentation using compression principles.

A new approach enhances the effectiveness of deep learning in solving PDEs.

Research highlights how feature learning improves neural network performance effectively.

A look at how different representations in AI improve understanding.

A look into how CNNs interpret images and their features.

SimCLR enhances model training using unlabeled data in vision tasks.

Learn how superpixel segmentation makes image analysis easier for machines.

Learn how Layer-Adaptive State Pruning improves deep learning models.

Combining RGB and depth data enhances part recognition in manufacturing.

Understanding how deep learning models handle uncertainty is crucial for accurate predictions.

This article discusses the role of surrogate losses in solving complex machine learning problems.

Trap-MID offers a clever way to protect data from hackers.

NeuralDEM speeds up particle simulation using deep learning for better engineering results.

A look at weight-averaged model-merging and its impact on deep learning performance.

A novel approach enhances understanding of neuron interactions and their role in brain behavior.

Discover how the Gauss-Newton matrix enhances neural network training efficiency.

This study highlights the importance of mid-level activations in AI models.

AE-DENet improves data communication through advanced channel estimation methods.

A new method simplifies pruning deep learning models using principles from physics.

ASiM framework enhances accuracy in energy-efficient deep learning technologies.

Bad data can lead to poor model performance in deep learning applications.

Milabench provides tailored benchmarks to improve AI performance evaluations.

TP-UNet enhances medical image segmentation by utilizing temporal information.

xIELU offers a promising alternative to traditional activation functions in deep learning.

Scientists apply deep learning to predict outcomes of heavy-ion collisions.

New method enhances deep learning security with random neural fingerprints.

Discover how EAST optimizes deep neural networks through effective pruning methods.

GCA-HNG improves model training by creating challenging negative samples.