# Chainer: A Deep Learning Framework for Accelerating the Research Cycle

arXiv: https://arxiv.org/abs/1908.00213 · HTML: https://mlsanity.com/paper/1908.00213/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 22 of 22)

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- [Analysis of memory consumption by neural networks based on hyperparameters](https://mlsanity.com/paper/2110.11424/) (2021)
- [Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs](https://mlsanity.com/paper/2201.09210/) (2022)
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- [HumanACGAN: conditional generative adversarial network with human-based auxiliary classifier and its evaluation in phoneme perception](https://mlsanity.com/paper/2102.04051/) (2021)
- [Pixyz: a Python library for developing deep generative models](https://mlsanity.com/paper/2107.13109/) (2021)
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- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)
- [Zenkai -- Framework For Exploring Beyond Backpropagation](https://mlsanity.com/paper/2311.09663/) (2023)

## Abstract (arXiv)

Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.