GENERATION: An Efficient Denoising Autoencoders-Based Approach for Amputated Image Reconstruction

Abstract

Missing values in datasets pose a significant challenge, often leading to biased analyses and suboptimal model performance. This study shows a way to fill in missing values using Denoising AutoEncoders (DAE), a type of artificial neural network that is known for being able to learn stable ways to represent data. The observed data are used to train the DAE, and then they are used to fill in missing values. Extensive tests on different image datasets, taking into account different mechanisms of missing data and percentages of missingness, are used to see how well this method works. The results of the experiments show that the DAE-based imputation works better than other imputation methods, especially when it comes to handling informative missingness mechanisms.