In order to detect objects within an image, artificial neural networks require careful design by experts over years of difficult research. They later address one specific task, such as to find what’s in photograph, to call genetic variant, or to help diagnose disease. Google believes one approach to generate these ANN architectures is through the use of evolutionary algorithms. So, today Google introduced AmoebaNets, an evolutionary algorithm that achieves state-of-the-art results for datasets such as ImageNet and CIFAR-10.
Google offers AmoebaNets as an answer to questions such as,
These questions were addressed through the two papers:
One important feature of the evolutionary algorithm (AmoebaNets) that the team used in their second paper is a form of regularization, which means:
Read more about AmoebaNets on Google Research Blog
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