2023/07/12 by M. Eren Akbiyik, Akbiyik, M. Eren · 12 citations
Computer Science · #AI in cancer detection #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Geography #Image (mathematics) #Machine Learning (cs.LG) #Noise (video) #Pattern recognition (psychology) #Training (meteorology)
paper · pdf · doi:10.48550/arxiv.2307.06855
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduce some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.