Recent Advances in Convolutional Neural Networks

TL;DR

This paper surveys recent progress in convolutional neural networks, highlighting improvements in design, training, and applications across vision, speech, and language tasks, driven by data growth and hardware advances.

Contribution

It provides a comprehensive overview of recent developments in CNN architectures, training methods, and diverse applications, summarizing key advancements and trends.

Findings

Significant improvements in CNN layer design and activation functions.

Enhanced training techniques leading to faster convergence.

Widespread application of CNNs in vision, speech, and NLP.

Abstract

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.