Understanding of machine learning with deep learning: architectures, workflow, applications and future directions
MM Taye - Computers, 2023 - mdpi.com
In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …
the field of machine learning (ML), achieving exceptional results on a variety of complex …
[HTML][HTML] Review of image classification algorithms based on convolutional neural networks
L Chen, S Li, Q Bai, J Yang, S Jiang, Y Miao - Remote Sensing, 2021 - mdpi.com
Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …
emergence of deep learning has promoted the development of this field. Convolutional …
Perceiver: General perception with iterative attention
Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …
Efficient deep learning: A survey on making deep learning models smaller, faster, and better
G Menghani - ACM Computing Surveys, 2023 - dl.acm.org
Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …
understanding, speech recognition, information retrieval, and more. However, with the …
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
Contrastive learning of global and local features for medical image segmentation with limited annotations
A key requirement for the success of supervised deep learning is a large labeled dataset-a
condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) …
condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) …
Deep semantic segmentation of natural and medical images: a review
The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …
instance, where each instance corresponds to a class. This task is a part of the concept of …
A survey of the recent architectures of deep convolutional neural networks
Abstract Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to Computer …
which has shown exemplary performance on several competitions related to Computer …
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning
A large number of children die due to pneumonia every year worldwide. An estimated 1.2
million episodes of pneumonia were reported in children up to 5 years of age, of which …
million episodes of pneumonia were reported in children up to 5 years of age, of which …