Exploring the Latent Space of Chinese Handwriting. These images were created by a deep convolutional generative adversarial network (DCGAN) trained on a database of handwritten Chinese characters, made with code by Alec Radford based on the paper by Radford, Luke Metz, and Soumith Chintala in November 2015.
The title is a reference to the 1988 book by Xu Bing, who composed thousands of fictitious glyphs in the style of traditional Mandarin prints of the Song and Ming dynasties.
A DCGAN is a type of convolutional neural network which is capable of learning an abstract representation of a collection of images. It achieves this via competition between a "generator" which fabricates fake images and a "discriminator" which tries to discern if the generator's images are authentic (more details). After training, the generator can be used to convincingly generate samples reminiscent of the originals.

Gene Kogan

Single Character Loops #29


Description

This 2015 work is the result of an early experiment in generative AI, where a neural network was trained to understand and recreate the essence of handwritten Chinese characters. Drawing on a database of nearly a million handwritten characters, the model learns to distill their patterns, forms, and textures, ultimately producing entirely new characters that never existed in the original dataset.

The title is a reference to the 1988 book by Xu Bing, who created thousands of fictitious glyphs in the style of ancient Chinese prints from the Song and Ming dynasties. In a similar way, this AI-driven exploration blurs the line between authentic tradition and imaginative creation, inviting us to reflect on the boundaries of written language.

Blockchain
Ethereum
Token standard
ERC-721
Contract address
0xb1...1588
Token
29
Set
Basel Set #1
Activity
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