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Image generation has its own problems with non-cancelling noise. For example, images are often generated with jpeg artifacts in regions but not globally. Wate
by pvillano 2y ago
Image generation has its own problems with non-cancelling noise.
For example, images are often generated with jpeg artifacts in regions but not globally.
Watermarks are also reproduced.
Some generated images have artifacts from CCD cameras
https://www.eso.org/~ohainaut/ccd/CCD_artifacts.html https://www.eso.org/~ohainaut/ccd/CCD_artifacts.html
Images generated from Google Street View data would likely contain features specific to the cars/cameras used in each country
https://www.geometas.com/metas/categories/google_car/ https://www.geometas.com/metas/categories/google_car/
- doctorpangloss 2y agoIt seems like such an obvious and surmountable problem though. Indeed since 2020 there are robust approaches to eliminating JPEG artifacts, for example - browse around here - https://openmodeldb.info/ https://openmodeldb.info/.
- pvillano 2y agoYou're right. In order to be a big problem, the error needs to be non-cancelling and inseparable. That makes me wonder: if you label good data, and generate data with the good label, how much benefit do you get from also training on okay data?