第1111章放大诡异
Amplifying The Uncanny
摘要：
深度神经网络已经变得非常擅长制作逼真的深度伪造，即（未经训练的眼睛）无法与真实图像区分的人的图像。Deepfake是由学习区分真实和虚假图像的算法产生的，并经过优化以生成系统认为真实的样本。这篇论文以及由此产生的一系列艺术品被挫败，探索了逆转这一过程的美学结果，而是优化系统来生成它预测为假的图像。这最大化了数据的不可能性，反过来又放大了这些机器幻觉的神秘性质。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
