Latest Articles for Deep Learning

Exploring new methods to improve image segmentation and instance detection.

A look into Cross-Task Linearity and its effects on model performance.

A new method improves EEG data compression while ensuring quality and efficiency.

Exploring how MRRs are reshaping computing for deep learning tasks.

A new method improves image generation from varied input views.

A new method improves machine learning model predictions through a teacher-student system.

Using deep reinforcement learning for better data traffic management.

This article introduces a unique method for clearer images by reducing noise effectively.

A new approach to deep learning that improves efficiency and stability.

This study investigates adversarial attacks on deep learning, focusing on CNN performance.

A study uses deep learning to classify spiral and ring galaxies.

Cannikin improves DNN training efficiency in diverse machine environments.

Simulated Overparameterization enhances model training while maintaining efficiency.

A study explores new methods for deep learning models to adapt to changing data.

A method enhances image quality by tackling demosaicing and denoising together.

This study highlights improvements in geometric models, focusing on expressiveness and completeness.

An analysis of reproducibility issues in deep learning software fault prediction research.

Investigating how label augmentation affects learning in machine learning models.

This article discusses the SpongeNet Attack and its impact on energy consumption in deep learning models.

A new model improves classification of radar signals from land and sea.

This paper presents a framework for validating deep learning methods in medical image analysis.

OIFTrack enhances tracking accuracy by managing information flow between target and background tokens.

A new method enhances indoor location accuracy through innovative frequency band analysis.

A new method enhances low-quality 3D images in electron microscopy using deep learning.

Learn how new pruning methods enhance efficiency in deep neural networks without sacrificing accuracy.

A new approach improves visual recognition tasks through residual attention mechanisms.

Introducing FOMO, a method to improve DNNs against adversarial attacks through forgetting.

A new method improves audio and sheet music matching.

A new method for faster and better radio environment map predictions using deep learning.

A new approach enhances the detection of unseen data in machine learning models.

Improving image quality in X-ray CT through targeted loss functions.

A look into the significance of the Local Learning Coefficient in machine learning models.

This study presents an attention-based model for estimating room volumes from audio recordings.

ASCA model enhances audio classification accuracy for small datasets.

New methods improve accuracy in identifying tumors from PET-CT scans.

This research sheds light on how the brain processes dynamic visual scenes.

A new approach improves historical solar data for better research.

Examining grokking in deep learning and its implications for performance.

New bounds for GNNs enhance performance against adversarial attacks.

New techniques enhance quantization while managing outliers for better model performance.