These notably include sparse coding methods and deep neural networks.
For X. tropicalis we estimate there to be 20,000 21,000 protein coding genes using "homology based gene prediction methods and deep [expressed sequence] resources" (Hellsten et al., 2010).
By reading a large amount of literature, the text feature extraction method and deep learning method is summarized.
Utilising a combination of spacer method and deep reactive ion etching presented in the previous section, fabricated silicon nanostructures is illustrated in Figure2, showing an array (Figure2a) and a close-up view (Figure2b) of the formed nanostructures.
Figure 2 SEM images of (a) an array; and (b) a close-up view of silicon nanostructures formed with a combination of spacer method and deep reactive ion etching.
Both the traditional non-deep-learning-based methods and the deep-learning-based ones have their advantages.
For convenience, we roughly subdivide the single-image SR methods into two subclasses: the non-deep-learning-based methods and the deep-learning-based ones.
In this paper, we establish the relationship between the traditional sparse-representation-based single-image super-resolution methods and the deep-learning-based ones and use transfer learning to make our proposed deep network take the image prior into account.
This study compared four mainstream classical machine learning methods and one deep learning method.
The proposed method is applied to analyze the experimental bearing vibration signals, and the results confirm that the proposed method is superior to the traditional methods and standard deep learning methods.
