A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition

Recent developments in deep learning have underscored prizing effective feature extraction in scenarios with limited or unlabeled data. This study introduces a novel unsupervised multi-scale feature extraction framework based on a multi-branch auto-encoder architecture. The proposed method decomposes input images into smooth, detailed and residual components, using variational loss functions to ensure that each branch captures distinct and non-overlapping representations. This decomposition enhances the information richness of input data while preserving its structural integrity, making it especially beneficial for grayscale or low-resolution images. Experimental results on classification and image segmentation tasks show that the proposed method enhances model performance by enriching input representations. Its architecture is scalable and adaptable, making it applicable to a wide range of machine learning tasks beyond image classification and segmentation. These findings highlight the proposed method’s utility as a robust, general-purpose solution for unsupervised feature extraction and multi-scale representation learning.