2022
1'30" video interpolation
HD/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.
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- PriceUSD PriceQuantityExpirationFrom
- PriceUSD PriceQuantityFloor DifferenceExpirationFrom
8๐ฌ7393๐ญ1-๐ช795-447๐ฌ-8๐ช๐ฏ๐ฎ-๐ฏ๐ฎ3๐ฏ75๐ฏ๐ซ๐ฎ๐ฌ๐ฏ3
- PriceUSD PriceQuantityExpirationFrom
- PriceUSD PriceQuantityFloor DifferenceExpirationFrom
2022
1'30" video interpolation
HD/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.