ITU

Deconstructing Representation

Research output: Non-textual form2D/3D (psysical products)Research

Standard

Deconstructing Representation. Lee, Rosemary (Producer). 2019. LOKALEEvent: Perpetual Interpreter, LOKALE, Copenhagen, Denmark.

Research output: Non-textual form2D/3D (psysical products)Research

Harvard

Lee, R, Deconstructing Representation, 2019, 2D/3D (psysical products), LOKALE.

APA

Lee, R. (Producer). (2019). Deconstructing Representation. 2D/3D (psysical products), LOKALE. Retrieved from http://interpreter.works

Vancouver

Author

Bibtex

@misc{dc90c50ebb5d41fe8f8dc057198888bc,
title = "Deconstructing Representation",
abstract = "In Deconstructing Representation machine learning is used as an explorative approach to visualise patterns within a dataset of images. Compiling a dataset of Instagram images saved by the artist over time and training a generative adversarial network (GAN) to produce images reminiscent of that dataset, the resulting images show the gradual buildup and breakdown of computational representations and visual affinities.",
author = "Rosemary Lee",
year = "2019",
month = nov
day = "8",
language = "English",
publisher = "LOKALE",
note = "Perpetual Interpreter ; Conference date: 08-11-2019 Through 24-11-2019",
url = "http://interpreter.works",

}

RIS

TY - ADVS

T1 - Deconstructing Representation

A2 - Lee, Rosemary

PY - 2019/11/8

Y1 - 2019/11/8

N2 - In Deconstructing Representation machine learning is used as an explorative approach to visualise patterns within a dataset of images. Compiling a dataset of Instagram images saved by the artist over time and training a generative adversarial network (GAN) to produce images reminiscent of that dataset, the resulting images show the gradual buildup and breakdown of computational representations and visual affinities.

AB - In Deconstructing Representation machine learning is used as an explorative approach to visualise patterns within a dataset of images. Compiling a dataset of Instagram images saved by the artist over time and training a generative adversarial network (GAN) to produce images reminiscent of that dataset, the resulting images show the gradual buildup and breakdown of computational representations and visual affinities.

M3 - 2D/3D (psysical products)

PB - LOKALE

T2 - Perpetual Interpreter

Y2 - 8 November 2019 through 24 November 2019

ER -

ID: 84694302