mlsanityData to 13 Oct 2026

Learning to See: You Are What You See

arXiv 2003.00902 ↗First posted 28 Feb 2020cs.CV cs.GR cs.HCcited by 4

Not on the map. Why?

Abstract (arXiv)

The authors present a visual instrument developed as part of the creation of the artwork Learning to See. The artwork explores bias in artificial neural networks and provides mechanisms for the manipulation of specifically trained for real-world representations. The exploration of these representations acts as a metaphor for the process of developing a visual understanding and/or visual vocabulary of the world. These representations can be explored and manipulated in real time, and have been produced in such a way so as to reflect specific creative perspectives that call into question the relationship between how both artificial neural networks and humans may construct meaning.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 28 Feb 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 2.

4 papers.