2022
30" video interpolation
4K/60fps
Model training: 11000 steps
Machine Learning: StyleGAN2
Model: #subjectiveconsistency
Dataset: the complete documentation of my physical art practice from 2008 until January 2021.
The components of the dataset can be found on cibellecavallibastos.xyz under Bodies of Work/ Physical. It includes sculptures, paintings, text work, ceramics, textiles, watercolours and collages.
Subjective Consistency is a body of work made in cooperation with Machine Learning.
The #subjectiveconsistency model was trained using the complete documentation of my physical art practice from 2008 until January 2021, when the model training had begun.
I select pieces from the generated images and curate the interpolation order. The latent walk is the ML model's decision entirely.
The intention of this project is to investigate a "subjective consistent" practice using Machine Learning, and through human-machine collaborations, attempt to image the aesthetic consistency derived beyond form.
6𝒻49𝒹233-00ℯ5-4𝒶74-𝒶467-𝒸9529𝒹794413
- PriceUSD PriceQuantityExpirationFrom
- PriceUSD PriceQuantityFloor DifferenceExpirationFrom
6𝒻49𝒹233-00ℯ5-4𝒶74-𝒶467-𝒸9529𝒹794413
- PriceUSD PriceQuantityExpirationFrom
- PriceUSD PriceQuantityFloor DifferenceExpirationFrom
2022
30" video interpolation
4K/60fps
Model training: 11000 steps
Machine Learning: StyleGAN2
Model: #subjectiveconsistency
Dataset: the complete documentation of my physical art practice from 2008 until January 2021.
The components of the dataset can be found on cibellecavallibastos.xyz under Bodies of Work/ Physical. It includes sculptures, paintings, text work, ceramics, textiles, watercolours and collages.
Subjective Consistency is a body of work made in cooperation with Machine Learning.
The #subjectiveconsistency model was trained using the complete documentation of my physical art practice from 2008 until January 2021, when the model training had begun.
I select pieces from the generated images and curate the interpolation order. The latent walk is the ML model's decision entirely.
The intention of this project is to investigate a "subjective consistent" practice using Machine Learning, and through human-machine collaborations, attempt to image the aesthetic consistency derived beyond form.