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Teaching Machines to Draw | 大トロ

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This is an updated version of my article, cross-posted on the Google Research Blog. Instructions on using the sketch-rnn model is available at Google Brain Magenta Project. Link to our paper, “A Neural Representation of Sketch Drawings”. This article has also been translated to Simplified Chinese. Recently, there have been major advancements in generative modelling of images using neural networks as a generative tool. While there is a already a large body of existing work on generative modelling of images using neural networks, most of the work thus far has been targeted towards modelling low resolution, pixel images. Humans, however, do not understand the world as a grid of pixels, but rather develop abstract concepts to represent what we see. From a young age, we develop the ability to communicate what we see by drawing on a piece of paper with a pencil. In this way we learn to express a sequential, vector representation of an image as a short sequence of strokes. In this work, we in

Latent space interpolation of various vector drawings produced by sketch-rnn . GitHub This is an updated version of my article, cross-posted on the Google Research Blog . Instructions on using the sketch-rnn model is available at Google Brain Magenta Project . Link to our paper, “ A Neural Representation of Sketch Drawings ”. This article has also been translated to Simplified Chinese . Introduction Vector drawings produced by our model. Recently, there have been major advancements in generative modelling of images using neural networks as a generative tool. While there is a already a large bo

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