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Location Conditional Image Generation using Generative Adversarial Networks

Can an AI-artist instil the emotion of sense of place in its audience? Motivated by this thought, this paper presents our endeavours to make a GANs model learn the visual characteristics of locations to achieve creativity. The project’s novelty lies in addressing the problem of the hardness of GANs training for an extremely diverse dataset in a contextual setting. The project explores GANs as an impressionist artist who adds its perspective to the artwork without hampering photo realism.

Mayur Saxena, Aidan Gomez, Yarin Gal
Machine Learning for Creativity and Design NeurIPS 2019 Workshop

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