Using Deep Image Priors to Generate Counterfactual Explanations
Published: Jun 6, 2021
Abstract
Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Though DIP inversion has been known to be superior to conventional regularized inversion strategies such as total variation, such an over-parameterized generator is able to effectively reconstruct even images that are not in the original data distribution. This...
Paper Details
Title
Using Deep Image Priors to Generate Counterfactual Explanations
Published Date
Jun 6, 2021
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