卷积神经网络
文艺复兴
深度学习
人气
计算机科学
人工智能
人工神经网络
模式识别(心理学)
机器学习
上下文图像分类
数据科学
特征(语言学)
深层神经网络
图像(数学)
特征提取
MNIST数据库
Softmax函数
心理学
历史
艺术史
社会心理学
作者
Waseem Rawat,Zenghui Wang
摘要
Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.
科研通智能强力驱动
Strongly Powered by AbleSci AI