# Losses, Dissonances, and Distortions

arXiv: https://arxiv.org/abs/2111.05128 · HTML: https://mlsanity.com/paper/2111.05128/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Performing Structured Improvisations with pre-trained Deep Learning Models](https://mlsanity.com/paper/1904.13285/) (2019)
- [Shared Visual Abstractions](https://mlsanity.com/paper/1912.04217/) (2019)
- [GANterpretations](https://mlsanity.com/paper/2011.05158/) (2020)

## Abstract (arXiv)

In this paper I present a study in using the losses and gradients obtained during the training of a simple function approximator as a mechanism for creating musical dissonance and visual distortion in a solo piano performance setting. These dissonances and distortions become part of an artistic performance not just by affecting the visualizations, but also by affecting the artistic musical performance. The system is designed such that the performer can in turn affect the training process itself, thereby creating a closed feedback loop between two processes: the training of a machine learning model and the performance of an improvised piano piece.