jh-jeong/smoothing-multiscale
Code for the paper "Multi-scale Diffusion Denoised Smoothing" (NeurIPS 2023)
This project offers a method to create more reliable image classification models by making them robust to minor input variations while maintaining high accuracy. It takes pre-trained image classifiers and training data, applies a multi-scale denoising technique, and outputs a more resilient model. This is for machine learning engineers or researchers who need to build robust vision systems.
No commits in the last 6 months.
Use this if you need to build image classification models that are highly accurate on clean data and also very robust to subtle, adversarial manipulations or noisy inputs.
Not ideal if you are looking for a plug-and-play solution for non-image data or if you lack the significant computational resources (GPUs) required for fine-tuning large models.
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15
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1
Language
Python
License
MIT
Category
Last pushed
Apr 30, 2024
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