Some new trends of deep learning research

Abstract

Deep learning has been attracting increasing attention in the recent decade throughout science and engineering due to its wide range of successful applications. In real problems, however, most implementation stages for applying deep learning still require inevitable manual interventions, which naturally conducts difficulty in its availability to general users with less expertise and also deviates from the intelligence of humans. It is thus a challenging while critical issue to enhance the level of automation across all elements of the entire deep learning framework, like input amelioration, model designing and learning, and output adjustment. This paper tries to list several representative issues of this research topic, and briefly describe their recent research progress and some related works proposed along this research line. Some specific challenging problems have also been presented.