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Yann Le Cun improved upon the original design in 1989 by using backpropagation to train models to recognize handwritten digits. In 2018, we saw novel architecture designs that improve upon performance benchmarks and also expand the range of media that machine learning models can analyze.
We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.
Google Brain researchers seek an answer to the question: do adversarial examples that are not model-specific and can fool different computer vision models without access to their parameters and architectures, can also fool time-limited humans?
However, it is still an open question whether humans are prone to similar mistakes.
Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system.
The method consists of two steps: stylization and smoothing.
Extensive experiments show that the suggested approach generates more realistic and compelling images than previous state-of-the-art.
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
While several photorealistic image stylization methods exist, they tend to generate spatially inconsistent stylizations with noticeable artifacts.
We’ve done our best to summarize these papers correctly, but if we’ve made any mistakes, please contact us to request a fix.
Special thanks also goes to computer vision specialist Rebecca Bur Wei for generously offering her expertise in editing and revising drafts of this article.