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Chainer: A Deep Learning Framework for Accelerating the Research Cycle

arXiv 1908.00213 ↗First posted 1 Aug 2019cs.LG cs.CV cs.DCcited by 22

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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.

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

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Citing papers per year: 2021: 10; 2022: 6; 2023: 5; 2024: 0; 2025: 1.

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  1. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 309
  2. 2021Torch.fx: Practical Program Capture and Transformation for Deep Learning in Pythoncited by 40
  3. 2022Flashlight: Enabling Innovation in Tools for Machine Learningcited by 25
  4. 2021ESPnet2-TTS: Extending the Edge of TTS Researchcited by 22
  5. 2021LazyTensor: combining eager execution with domain-specific compilerscited by 11
  6. 2021Ivy: Templated Deep Learning for Inter-Framework Portabilitycited by 6
  7. 2021Swift for TensorFlow: A portable, flexible platform for deep learningcited by 6
  8. 2022Optimizing Mixture of Experts using Dynamic Recompilationscited by 4
  9. 2022Memory Planning for Deep Neural Networkscited by 3
  10. 2023On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameterscited by 3
  11. 2023The Grand Illusion: The Myth of Software Portability and Implications for ML Progresscited by 3
  12. 2025The Oversmoothing Fallacy: A Misguided Narrative in GNN Researchcited by 3
10 more
  1. 2021Optimizer Fusion: Efficient Training with Better Locality and Parallelismcited by 2
  2. 2021SoftNeuro: Fast Deep Inference using Multi-platform Optimizationcited by 2
  3. 2021Analysis of memory consumption by neural networks based on hyperparameterscited by 1
  4. 2022Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programscited by 1
  5. 2023Auto-Differentiation of Relational Computations for Very Large Scale Machine Learningcited by 1
  6. 2021HumanACGAN: conditional generative adversarial network with human-based auxiliary classifier and its evaluation in phoneme perceptioncited by 0
  7. 2021Pixyz: a Python library for developing deep generative modelscited by 0
  8. 2022DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysiscited by 0
  9. 2023Unveiling the frontiers of deep learning: innovations shaping diverse domainscited by 0
  10. 2023Zenkai -- Framework For Exploring Beyond Backpropagationcited by 0