DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
This addresses reproducibility issues for researchers and practitioners in deep learning by automating code generation from papers.
The authors tackled the challenge of reproducing deep learning research by automatically generating code from design diagrams and tables in papers, achieving over 93% accuracy in extracting flow diagram content.
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.