DiffuNet: Enhancing Advanced ML Model Training for Class Equity

Abstract

"Training advanced machine learning (ML) models like Denoising Diffusion Probabilistic Models (DDPMs) is resource-intensive, limiting their practical use. The uniform application of diffusion steps, regardless of data complexity, make worse this inefficiency. This study introduces a novel framework to enhance DDPM training efficiency by dynamically adjusting diffusion steps based on image entropy, also utilizing DDPMs to tackle class imbalance issue in image datasets. The proposed solution employs a complexity assessment function C(x) to measure image entropy and a scaling function S(C(x), t) that adjusts diffusion steps and noise levels accordingly. This method optimizes the diffusion process for each image, improving DDPMs' training efficiency. Additionally, this approach utilizes the enhanced DDPMs to produce synthetic images for classes that are underrepresented, effectively balancing the dataset. The technical implementation leverages these advancements to mitigate the challenges posed by computational demands in DDPMs and class imbalance issues in image classification. Evaluations across multiple datasets, including MNIST, FashionMNIST, KMNIST, and QMNIST, underscore the effectiveness of this approach. By employing metrics such as the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), alongside assessments of training and sampling efficiency, the study demonstrates the proposed method's capacity to significantly reduce computational demands while maintaining or enhancing image generation quality. Moreover, the practical application of this approach in correcting class imbalance further validates its utility, achieving balanced datasets that lead to improved classification model performance across various metrics."