Jitao Sang
ABSTRACT
Deep learning has been successfully exploited in addressing different multimedia problems in recent years. The academic researchers are now transferring their attention from identifying what problem deep learning CAN address to exploring what problem deep learning CAN NOT address. This tutorial starts with a summarization of six 'CAN NOT' problems deep learning fails to solve at the current stage, i.e., low stability, debugging difficulty, poor parameter transparency, poor incrementality, poor reasoning ability, and machine bias. These problems share a common origin from the lack of deep learning interpretation. This tutorial attempts to correspond the six 'NOT' problems to three levels of deep learning interpretation: (1) Locating - accurately and efficiently locating which feature contributes much to the output. (2) Understanding - bidirectional semantic accessing between human knowledge and deep learning algorithm. (3) Expandability - well storing, accumulating and reusing the models learned from deep learning. Existing studies falling into these three levels will be reviewed in detail, and a discussion on the future interesting directions will be provided in the end.
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- Gary Marcus. 2018. Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631 (2018).Google Scholar
- Wojciech Samek and Klaus-Robert Müller. 2017. Tutorial on Interpretable Machine Learning. GCPR (2017).Google Scholar
- Guanhua Zheng, Jitao Sang, and Changsheng Xu. 2017. Understanding deep learning generalization by maximum entropy. arXiv preprint arXiv:1711.07758 (2017).Google Scholar
Index Terms
- Deep Learning Interpretation
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