论文标题
放大怪异
Amplifying The Uncanny
论文作者
论文摘要
深度神经网络已经变得非常擅长生产逼真的深击，（对未经训练的眼睛）与真实图像没有区别的人的图像。深层蛋白是由学会区分真实图像的算法产生的，并经过优化以生成系统认为现实的样品。本文及由此产生的一系列艺术品被挫败了，探索了颠倒此过程的美学结果，而是优化系统以生成预测为假货的图像。这最大化了数据的不可能，进而扩大了这些机器幻觉的不可思议性质。
Deep neural networks have become remarkably good at producing realistic deepfakes, images of people that (to the untrained eye) are indistinguishable from real images. Deepfakes are produced by algorithms that learn to distinguish between real and fake images and are optimised to generate samples that the system deems realistic. This paper, and the resulting series of artworks Being Foiled explore the aesthetic outcome of inverting this process, instead optimising the system to generate images that it predicts as being fake. This maximises the unlikelihood of the data and in turn, amplifies the uncanny nature of these machine hallucinations.
