Collection: Residual

30 pieces
  • Form and Matter, 2019
    Form and Matter, 2019
    Regular price
    €389.99
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    €389.99
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  • Going for a Walk, 2019
    Going for a Walk, 2019
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    €219.99
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    €219.99
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  • Goodbye, 2019
    Goodbye, 2019
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    €149.99
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  • Hey, You!, 2019
    Hey, You!, 2019
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    €219.99
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    €219.99
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  • Hug Me, 2019
    Hug Me, 2019
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    €299.99
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    €299.99
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  • I See You, 2019
    I See You, 2019
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    €389.99
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  • I'm Going Away, 2019
    I'm Going Away, 2019
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    €189.99
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  • In the forest, 2019
    In the forest, 2019
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    €199.99
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  • It's Time to Go, 2019
    It's Time to Go, 2019
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    €235.99
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  • Listen, 2019
    Listen, 2019
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    €149.99
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Residual

What will we leave to future generations? portraits of machine dreaming about animals that once inhabited Earth? Is this the only way to remember them, just a residual memory? 

An exploration of anxieties around the wildlife such as global warming, destroyed habitat, shortage of food, and human predatory practices. Our exclusive artist, diavlex, uses AI as a tool of expression to represent the residual image of  species affected by human predatory actions.

TL;DR of Our Approach

  • The core AI technique for this collection is our Neural Painter implementation from scratch. Neural Painters, introduced in [1], are Generative Models models able to paint using brushstrokes, in contrast to common pixel-level ones. We consider Neural Painters to be a more natural choice for Art+AI artwork and, therefore, our preferred choice.

  • We also use a tailor-made Non-Adversarial Super-Resolution model based on a dynamic U-Net architecture [2]. The approach is inspired by fast.ai's 'Decrappification' technique [3]. 

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References

[1] Neural Painters: A Learned Differentiable Constraint
for Generating Brushstroke Paintings. Reiichiro Nakano. Preprint. 2019. https://arxiv.org/abs/1904.08410

[2] U-Net: Convolutional Networks for Biomedical Image Segmentation. Olaf Ronneberger, Philipp Fischer, Thomas Brox. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9351: 234--241, 2015, available at arXiv:1505.04597

[3] Decrappification, DeOldification, and Super Resolution
Jason Antic (Deoldify), Jeremy Howard (fast.ai), and Uri Manor (Salk Institute). 2019. https://www.fast.ai/2019/05/03/decrappify/