Transparency of Deep Neural Networks

Dear Colleagues,

Deep learning is a new branch of machine learning which has been proven to be a powerful feature extraction tool in computer vision. The primary disadvantage of deep learning is that it has no clear declarative representation of knowledge. In addition, deep learning has considerable difficulties in generating the necessary explanation structures, which limits its potential because the ability to provide detailed characterizations of classification strategies would promote its acceptance. However, surprisingly, very little work has been carried out in relation to the transparecncy of deep learning. Bridging this gap could be expected to contribute to the real-world utility of deep learning. The transparency of deep neural networks is the first step towards filling this gap. The next step towards utilizing deep neural networks is rule extraction from deep neural networks. Transparency and rule extraction from deep neural networks, therefore, remain areas in need of further innovation.

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