Generative models as distributions of functions
Generative models are typically trained on grid-like data such as images. As a result, the size of these models usually scales directly with the underlying grid resolution. In this paper, we abandon discretized grids and instead parameterize individual data points by continuous functions. We then bu...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Journal of Machine Learning Research
2022
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