# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 4 Experimental Results
### Datasets  
The Mixed National Institute of Standards and Technology (MNIST) is a dataset of handwritten digit images widely used in machine learning and computer vision. As shown in Figure 3, the dataset consists of images of handwritten digits from 0 to 9. Each image is a monochrome image with a size of 28 \(\times\) 28 pixels. Each image is a 28 \(\times\) 28 pixel grayscale image represented by a pixel value between 0 and 255. The entire dataset consists of a total of 70,000 images, of which 60,000 belong to the training set and the remaining 10,000 belong to the test set. This dataset is used as a representative benchmark for number recognition problems. It is widely used to compare and evaluate the performance of machine learning algorithms. Since it has already been widely used, it performs well for classification tasks. Therefore, we thought it would perform well for domain adaptation. The number of data was too large for training.  
The United States Postal Service (USPS) dataset is a dataset of handwritten digit images collected from the United States Postal Service. This dataset is primarily used for recognizing handwritten zip codes. As shown in Figure 4, the data contains digit images similar in shape and organization to the aforementioned MNIST, consisting of black and white images with a size of 16 \(\times\) 16. The entire dataset contains a total of 9298 images, of which 7291 belong to the training set and the remaining 2007 belong to the test set. This dataset is used in applications such as address recognition and mail sorting. It can also be utilized for domain adaptation tasks.  
The Street View House Numbers (SVHN) dataset is a dataset of house number images taken in a street environment, as shown in Figure 5. This dataset is a collection of images containing numerical numbers of houses. It addresses the problem of number recognition in a realistic environment. The entire dataset consists of 604,388 images, of which 73,257 belong to the training set, 26,032 belong to the validation set, and the remaining 26,032 belong to the test set. The images were taken in a variety of street conditions, including complex backgrounds and lighting variations. The SVHN dataset is used to evaluate performance under realistic conditions. It is one of the most important datasets in the field of digit recognition.  
Figure 3: Samples of MNIST dataset.  
This dataset is a good dataset for number recognition. Thus, it was also selected in this study. However, since the dataset itself is taken from real photos, even though it is labeled, the performance of the dataset may vary depending on the preprocessing.  
The EMNIST (extended MNIST) dataset is a dataset of handwritten alphabet letters and numeric images. This dataset has a similar organization to MNIST, with a total of 814,255 images. Of these, 814,255 images are in the training set. The EMNIST dataset is used to recognize handwritten alphabet letters and numbers. It is commonly used to solve the problem of handwriting recognition. It is one of the most important datasets that can be applied to the problem of recognizing various alphabetic characters and numbers.  
As can be seen in Figure 6, it is an extension of the MNIST dataset. Thus, the number of data is very large compared to MNIST. The reason is that it consists of grayscale images that are easy to classify, such as numeric data from 0 to 9, letters from a to z, and so on. Therefore, it was thought that it would be best to run a domain adaptation experiment with MNIST.  
Figure 4: Samples of USPS dataset.  
Figure 5: Samples of SVHN dataset.