Subkash2206/spectral-aliasing-cnns
A spectral analysis framework for studying aliasing in strided CNNs. Defines the Alias Violation Ratio (AVR) and Shift Instability Score (SIS) to quantify Nyquist violations and their relationship to prediction instability.
This project provides a framework to understand why image classification models might give different answers when an image is shifted slightly. It takes your trained Convolutional Neural Network (CNN) and images as input, and outputs two scores: Alias Violation Ratio (AVR) and Shift Instability Score (SIS). These scores help machine learning researchers and practitioners understand and quantify how much internal aliasing contributes to prediction instability in their models.
Use this if you are a machine learning researcher or practitioner concerned about the robustness of your CNNs to small input shifts and want to diagnose the underlying causes related to spectral aliasing.
Not ideal if you are looking for an off-the-shelf solution for a specific image classification task without delving into the spectral properties of your model.
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Python
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Last pushed
Mar 14, 2026
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