mlsanityData to 14 Oct 2026

Recent Advances in Convolutional Neural Networks

arXiv 1512.07108 ↗First posted 22 Dec 2015cs.CV cs.LG cs.NEcited by 360

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Abstract (arXiv)

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.

Key facts. Cited by 360 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2015.

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Citing papers per year: 2021: 73; 2022: 79; 2023: 86; 2024: 67; 2025: 44; 2026: 11.

360 papers.

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