Here at SensiLab we are designing, implementing and evaluating improvisational interfaces - computer interfaces for improvising in creative arts domains. Ultimately we are interested in realising the notion of machines as creative collaborators, and as part of this overall goal we seek to implement improvisational behaviours in machine agents, facilitate improvised exchanges between humans and computers, and create guidelines for designing interfaces that support improvised interaction.

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Deep Convolutional Neural Networks (DCNNs) have made surprising progress in synthesising high-quality and coherent images. The best performing neural network architectures in the generative domain are the class of Generative Adversarial Networks (GANs). These networks have been applied in many different scenarios: domain transfer of images, e.g. going from sunny to winter environments; generating images from text descriptions; using computer generated data to train neural networks for real world settings; and many other domains.

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