A survey of convolutional neural networks: analysis, applications, and prospects
A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …
learning field. Since CNN made impressive achievements in many areas, including but not …
[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 …
Deep learning approaches for COVID-19 detection based on chest X-ray images
COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the
lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a …
lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a …
A discriminative feature learning approach for deep face recognition
Convolutional neural networks (CNNs) have been widely used in computer vision
community, significantly improving the state-of-the-art. In most of the available CNNs, the …
community, significantly improving the state-of-the-art. In most of the available CNNs, the …
[PDF][PDF] The computational limits of deep learning
Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …
in the game of Go to world-leading performance in image classification, voice recognition …
Imagenet large scale visual recognition challenge
Abstract The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object
category classification and detection on hundreds of object categories and millions of …
category classification and detection on hundreds of object categories and millions of …
Deepface: Closing the gap to human-level performance in face verification
In modern face recognition, the conventional pipeline consists of four stages: detect=>
align=> represent=> classify. We revisit both the alignment step and the representation step …
align=> represent=> classify. We revisit both the alignment step and the representation step …
Deep face recognition: A survey
Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …
multiple levels of feature extraction. This emerging technique has reshaped the research …
Past, present, and future of face recognition: A review
Face recognition is one of the most active research fields of computer vision and pattern
recognition, with many practical and commercial applications including identification, access …
recognition, with many practical and commercial applications including identification, access …
Deep learning face representation by joint identification-verification
The key challenge of face recognition is to develop effective feature representations for
reducing intra-personal variations while enlarging inter-personal differences. In this paper …
reducing intra-personal variations while enlarging inter-personal differences. In this paper …