Treffer: Differentiable Visual Computing: Challenges and Opportunities.

Classical algorithms typically contain domain-specific insights. This makes them often more robust, interpretable, and efficient. On the other hand, deep-learning models must learn domain-specific insight from scratch from a large amount of data using gradient-based optimization techniques. To have the best of both worlds, we should make classical visual computing algorithms differentiable to enable gradient-based optimization. Computing derivatives of classical visual computing algorithms is challenging: there can be discontinuities, and the computation pattern is often irregular compared to high-arithmetic intensity neural networks. In this article, we discuss the benefits and challenges of combining classical visual computing algorithms and modern data-driven methods, with particular emphasis to my thesis, which took one of the first steps toward addressing these challenges. [ABSTRACT FROM AUTHOR]

Copyright of IEEE Computer Graphics & Applications is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)