# A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
## 3 Experiments
### Experimental Configurations  
To evaluate the proposed method, several pre-trained network architectures that accept three-channel inputs were used. The ResNet-18 [50], ResNet-50 [51], and MobileNetV2 [52] architectures were used to analyze the impact of newly generated features on classification performance. ResNet-18 and ResNet-50 are deep residual networks designed to address the problem of vanishing gradient through residual connections, which allows the training of deep networks. ResNet-18 consists of 18 layers, while ResNet-50, with 50 layers, enables more complex feature extraction. MobileNetV2 is a lightweight architecture optimized for efficient performance on mobile and embedded devices. Using separable convolutions to reduce computational complexity while maintaining high classification accuracy.  
The evaluation process began with the extraction of the residual layer from the output of the proposed method. The smooth and detailed components were then merged with the original input to form a new three-channel representation, which served as input for the ResNet-18, ResNet-50, and MobileNetV2 networks to evaluate the classification performance. In addition, a separate test was conducted using the same networks, where no pre-preprocessing was applied to the input images. Instead, a grayscale version of each image was replicated across all three channels to form the input. The details of the hyperparameters used in the training process of the proposed model are shown in Table 3.  
In our experiments, \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) values for proposed loss functions were assigned fixed values to maintain consistency and ensure stable convergence during training. These values were empirically determined through preliminary experiments in a validation subset of the training data. The aim was to balance the influence of each branch so that the smooth and detail layers capture meaningful structural information while the residual layer suppresses noise or irrelevant patterns. Improper tuning of the regularization coefficients can lead to ineffective decomposition of the image features across the respective layers. In such cases, most of the image information can be gathered in a single layer, causing the other layers to become redundant or uninformative. This undermines the model's ability to decompose the image into smooth, detailed, and residual components as intended. This parameter tuning represents a challenging aspect of the proposed method. Therefore, careful adjustment of these parameters is essential to ensure that each layer captures distinct and meaningful features, ultimately enhancing the quality of feature extraction and improving model performance.  
Besides classification, the proposed method was further evaluated on an image segmentation task using a lung CT dataset. The pre-trained multibranch autoencoder model, originally trained on CIFAR-10, was used to decompose each CT image into smooth, detailed, and residual components. For this experiment, the residual layer was excluded, which is typically the source of redundant information and noise, and the original image was used in its place. These components were then combined and used as input to a U-Net [53] segmentation model. In addition, we performed an ablation study by training the segmentation network in different combinations of smooth layer, detail layer, and original image to validate the contribution of each component.  
These experiments assessed the impact of the proposed method on the extractable information from the data and evaluated how these newly generated representations influence the performance of machine learning models built upon them.