# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 4 Experimental Study
### Classification Accuracy Rates  
Our CNN-features were extracted as follows: We first prepared a CNN model using source training data, Data1 (# of epochs: 100). Next, we fine-tuned this pretrained model using target training data, Data2 (# of epochs: 200). Here, as for Data1, Train1 was used as it was, and Data2, of 10,000 patches (5000 per class), was randomly selected from RealPatch.  
In classification work performed by a feature extractor and a classifier, in general, there is no optimal feature extractor for all classifiers, and vice versa. Thus, to validate the performance of our CNN-based extractor, we conducted a simple classification experiment using only two types of classifiers: the k-nearest neighbor rules (kNNs: k = 1, 3), and SVMs (of the polynomial and RBF kernels). (Here, each of these two classifiers was chosen as the easiest to implement and the most widely used classifiers. Although kNNs have a long execution time, the classifiers were chosen because the discriminative characteristics of the input vectors can be directly reflected in the output determination. Therefore, thorough evaluation using other classifiers, such as AdaBoost and the decision tree, is a future challenge).  
Table 2 presents a numerical comparison of the classification accuracy rates (%) of the features extracted by the five methods, i.e., PCA, kPCA, AE, CNN-sum, and CNN-O2P. Here, the values were averaged after repeating 10 times. Each time, 10,000 (and 10,000) patches were randomly selected as the training (and test) data from RealPatch, and the five different types of feature vectors were extracted. For a fair comparison, the dimensions of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Input** & **Output** & **Feature** & **Kernel** \\
**Descriptions** & **Size** & **Size** & **Maps** & **Size** \\ \hline
1 st convolution and & & & & \\ subsampling & & & & \\
2 nd convolution and & & & & \\ subsampling & & & & \\ Fully connected & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the CNN architecture.  
all the feature vectors were adjusted to 256. The highest (second) accuracy of each row is emphasized in bold (underlined).  
From the comparison shown in Table 2, it can be observed that the use of the CNN-based extractor, in conjunction with existing classifiers, can significantly improve the classification accuracy compared to the other extractors included in the comparison. In particular, it is noteworthy that the CNN-based extractor uses not only training data, but also pretrained CNN models that could not be used for other extractions. Under this condition, a direct comparison of the five feature extractors may not be fair. However, from the perspective of machine learning, such as transfer learning, the results of this experiment suggest one possibility related to the \"new\" extractor. From this consideration, the performance improvements observed in the last column of the table may have resulted from the discriminating ability of the pretrained models.  
Finally, in Table 2, CNN-sum and CNN-O2P were included as feature extractors, but they can also serve as classifiers with the FC layers. The classification performances of the CNN-sum and CNN-O2P trained for the experiments in the table were 96.13 (%) and 96.61 (%), respectively.