isaaccorley/Making-Convolutional-Networks-Shift-Invariant-Again-Tensorflow
Tensorflow Implementation of BlurPool the Antialiasing Pooling operation from "Making Convolutional Networks Shift-Invariant Again"
This project helps machine learning practitioners improve the robustness and reliability of their image classification and analysis models. By integrating a specialized pooling operation, it takes standard image data and produces more stable, accurate model predictions, especially when dealing with slight shifts or movements in the input images. This is for data scientists and AI engineers who build and deploy computer vision systems.
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Use this if you are building convolutional neural networks for image processing and want to ensure your models are less sensitive to minor shifts or translations in the images they analyze.
Not ideal if your primary concern is with non-image data or if your existing image processing models already demonstrate sufficient shift invariance.
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Language
Python
License
GPL-3.0
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Last pushed
Aug 18, 2019
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