Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size.

ABSTRACT: Deep neural networks have gained immense popularity in the Big Data problem; however, the availability of training samples can be relatively limited in specific application domains, particularly medical imaging, and consequently leading to overfitting problems. This "Small Data" challenge may need a mindset that is entirely different from the existing Big Data paradigm. Here, under the small data scenarios, we examined whether the network structure has a substantial influence on the performance and whether the optimal structure is predominantly determined by sample size or data nature. To this end, we listed all possible combinations of layers given an upper bound of the VC-dimension to study how structural hyperparameters affected the performance. Our results showed that structural optimi

SUBMITTER: D'souza RN 

PROVIDER: S-EPMC6972775 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature