Results 61 to 70 of about 28,970 (210)

Data Dropout: Optimizing Training Data for Convolutional Neural Networks

, 2018
Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters.
Huan, Jun, Li, Bo, Wang, Tianyang
core   +1 more source

A General Framework for Knowledge Integration in Machine Learning for Electromagnetic Scattering Using Quasinormal Modes

Laser &Photonics Reviews, EarlyView.
Neural networks can accelerate modeling and inverse design of electromagnetic devices by several orders of magnitude, but usually require large amounts of data to train. This work demonstrates that integrating knowledge about quasinormal modes into the network architecture reduces the required amount of training data significantly, while simultaneously
Viktor A. Lilja, Albin J. Svärdsby, Timo Gahlmann, Philippe Tassin   +3 more
wiley   +1 more source

FFTL-Net: a network for the classification of skin diseases based on feature fusion and transfer learning

Machine Learning: Science and Technology
The objective is to address the issues of data imbalance, overfitting, and inadequate generalization ability in skin disease datasets and recognition models.
Xiaowei Song, Yurong Mei, Zhilei Zhao, Hao Chang, Lina Han, Hui Wang, Xinyi Zhang, Guoqiang Wang   +7 more
doaj   +1 more source

DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification

Frontiers in Systems Neuroscience, 2022
Aims: Brain diseases refer to intracranial tissue and organ inflammation, vascular diseases, tumors, degeneration, malformations, genetic diseases, immune diseases, nutritional and metabolic diseases, poisoning, trauma, parasitic diseases, etc.
Ziquan Zhu, Siyuan Lu, Shui-Hua Wang, Shui-Hua Wang, Juan Manuel Gorriz, Yu-Dong Zhang, Yu-Dong Zhang, Yu-Dong Zhang   +7 more
doaj   +1 more source

Data Augmentation for Skin Lesion Analysis

, 2018
Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited.
Avila, Sandra, Perez, Fábio, Valle, Eduardo, Vasconcelos, Cristina   +3 more
core   +1 more source

Survey on AI‐Enabled Computer Vision Technologies and Applications for Space Robotic Missions

Journal of Field Robotics, EarlyView.
ABSTRACT This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)‐enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration.
Maciej Quoos, Steven Kay, Julia Wajoras, Robert Field, Evridiki V. Ntagiou, Fakher Mohammad, Yang Gao   +6 more
wiley   +1 more source

Instance-based Deep Transfer Learning

, 2018
Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain.
Huan, Jun, Wang, Tianyang, Zhu, Michelle
core   +1 more source

Memory-Efficient Implementation of DenseNets

, 2017
Technical ...
Pleiss, Geoff, Chen, Danlu, Huang, Gao, Li, Tongcheng, van der Maaten, Laurens, Weinberger, Kilian Q.   +5 more
openaire   +2 more sources

DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms

Frontiers in Bioscience-Landmark
Background: m6Am is a specific RNA modification that plays an important role in regulating mRNA stability, translational efficiency, and cellular stress response.
Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang   +3 more
doaj   +1 more source

Advancements in Image Classification using Convolutional Neural Network

, 2019
Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification.
Dutta, Paramartha, Sufian, A., Sultana, Farhana   +2 more
core   +1 more source