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arXiv · 1711.03543

DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers

Akshay Sethi·Anush Sankaran·Naveen Panwar·Shreya Khare·Senthil Mani
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Abstract

With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5,000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our approach, we create a simulated dataset with over 216,000 valid design visualizations using a manually defined grammar. Experiments on the simulated dataset show that the proposed framework provide more than $93\%$ accuracy in flow diagram content extraction.

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Akshay Sethi, Anush Sankaran, Naveen Panwar, Shreya Khare, Senthil Mani. 2017-11-09. DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers. https://arxiv.org/abs/1711.03543

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