# Deep Feature Retention Module Network for Texture Classification
## 1 Introduction  
Texture is an important image characteristic in various fields [1; 2]. In particular, texture provides meaningful cues for recognizing features in images [3; 4]. To examine these characteristics, general object-based and texture-based datasets are compared with each other as follows: object-based datasets, such as ImageNet [5], CIFAR100 [6], and CUB-200-2011 [7], have been used to train the general model. Figure 0(a) shows examples of images from CUB-200-2011, in which images of 200 different objects were collected. For example, the first and third rows can be distinguished because Bobolink and Florida Jay have different characteristics. Similarly, during the training process, an object-based dataset can be used to determine the distinguishing characteristics between various objects and then classify those objects. However, in contrast to object-based datasets, texture-based datasets consist of unique image patterns. The classes in these datasets contain images that do not belong to the same objects. As shown in Figure 0(b), which shows examples of images in the describable textures dataset (DTD) [8], most images in each class are made of the microparts of the objects, showing their material pattern. Because CNN-based models exhibit high performance in the ImageNet competition, various studies on deep learning have been conducted in computer vision. The typical deep learning models include AlexNet [9], VGG [10], ResNet [11], DenseNet [12], and EfficientNet [13]. These models commonly consist of a convolutional neural network (CNN) to learn data characteristics while maintaining the input data characteristics and preventing information loss during learning [14; 15]. During the learning process, insufficient data can affect the accuracy of the model. One solution to this problem is transfer learning [16]. Using transfer learning, models can be pretrained on publicly available large datasets, such as ImageNet, which can be further trained on the target dataset with fewer data. Additionally, fine-tuning techniques can be used to adjust the structure of the pretrained model to suit the current purpose. Because texture datasets lack the number of images and classes, a model structure should be constructed to identify the features of the texture dataset by fine-tuning. As CNN models suffer from the problem of vanishing gradients with increasing layer depth, it is difficult to expect high-performance results when using a typical model structure. Thus, CNN-based models that classify texture datasets commonly use structures that retain the characteristics of transfer learning models. For example, ref. [17] presented a FASON model with a module that combined features learned from multiple-level information. To maintain the detailed characteristics of the texture, ref. [18] presented a learnable encoding module (LEM) that combined the low-to-high-level features of a CNN. The models proposed in [17; 18] commonly applied additional module types that preserved texture information using pretrained models to preserve different layer features and minimize loss. These additional features result in an improvement in the classification performance between classes. In contrast to object classification, texture classification is a method for identifying unique patterns in materials in images. Various experiments have shown that it is advantageous to extract the comprehensive characteristics of an image and use them together to determine common regularities in the diversity of texture patterns. In addition, it has been demonstrated that extracting and using detailed feature information from the output of various levels results in performance improvement [3; 18]. Therefore, in this study, we propose a new Deep Feature Retention Module Network to classify texture datasets. First, inspired by the structure proposed in [18], various level characteristics were utilized.