DARVIZ: Deep Abstract Representation, Visualization, and Verification of Deep Learning Models
This addresses the problem of managing complex, data-driven software development for researchers and engineers in deep learning, though it appears incremental in improving existing tools.
The paper tackles the challenge of visualizing, interpreting, and ensuring interoperability in deep learning models across multiple programming libraries, proposing DARVIZ as a solution for deep abstract representation, visualization, and verification.
Traditional software engineering programming paradigms are mostly object or procedure oriented, driven by deterministic algorithms. With the advent of deep learning and cognitive sciences there is an emerging trend for data-driven programming, creating a shift in the programming paradigm among the software engineering communities. Visualizing and interpreting the execution of a current large scale data-driven software development is challenging. Further, for deep learning development there are many libraries in multiple programming languages such as TensorFlow (Python), CAFFE (C++), Theano (Python), Torch (Lua), and Deeplearning4j (Java), driving a huge need for interoperability across libraries.