# A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
## 1 Introduction  
In recent years, rapid technological advances have led to a dramatic increase in the volume of accessible data. This surge in data availability has contributed significantly to the development of computer-based systems designed for a wide range of purposes, particularly those that aim to improve human well-being through intelligent decision making, automation, and data-driven optimization. Moreover, the growing abundance of data and the increasing computational power have sped up the progress of artificial intelligence research, especially in deep learning, enhancing its capabilities and enabling its widespread adoption in almost all domains, such as image classification [1, 2], denoising [3], object detection [4, 5], image segmentation [6, 7], decision support systems [8, 9], medical imaging [10, 11], facial expression recognition [12, 13], and remote sensing [14, 15]. However, the diverse range of problems addressed by machine learning models often introduces significant complexity and analytical challenges, with model performance still dependent on the quality and relevance of features extracted from raw data. The performance of the model remains dependent on the quality and relevance of the features extracted from the raw data. Moreover, real-world data are rarely perfect and often contain issues such as noise, missing values, or low resolution. Limited data, such as grayscale images, low-quality signals, or abbreviated text, constrains the extractable features in some situations. These limitations make it more difficult for machine learning models to perform, as the quality of input features plays a crucial role in the learning process. Consequently, robust and effective feature extraction is important for improving the performance and success of machine learning models. Researchers have developed many traditional feature extraction techniques based on mathematical descriptors to derive meaningful information from data. Notable examples include Histogram of Oriented Gradients (HOG) [16], Scale-Invariant Feature Transform (SIFT) [17], Gabor filters [18], Gaussian filters [19], and Local Binary Patterns (LBP) [20]. These methods are computationally efficient and well-suited for scenarios with limited computational resources or small-scale datasets. However, their performance is often constrained by their reliance on manually designed features, which may not generalize well across diverse tasks or data modalities. With the advent of deep learning, feature extraction has undergone a major transformation. Modern approaches now enable models to automatically learn hierarchical and task-specific representations directly from raw data. Among the most widely used architectures are Convolutional Neural Networks (CNNs) [21], which use convolutional filters to learn spatial features and are widely applied to image, video, and speech data; Transformers [22], which utilize self-attention mechanisms to model long-range dependencies and have demonstrated impressive performance in various vision tasks; and Autoencoders [23], which compress input data into low-dimensional embeddings through unsupervised encoder-decoder frameworks. Within these deep learning paradigms, multi-scale feature extraction has emerged as a critical strategy, especially for visual tasks that require a simultaneous understanding of both global structures and local fine-grained patterns. The hierarchical architecture of CNNs naturally supports multi-scale analysis, enabling the model to learn feature representations at different levels of abstraction. [PERSON] et al. [24] demonstrated the value of this approach in medical image classification, while [PERSON] et al. [25] proposed a deep learning multi-route feature extraction architecture for improved breast tumor segmentation. Domain-specific implementations of multi-scale methods further emphasize their practical effectiveness. [PERSON] et al. [26] introduced a texture-aware CNN for plant disease classification in agricultural images. [PERSON] et al.