Comment by ipunchghosts
7 hours ago
Why is this not a typical data augmentation method for training deep networks? It would allow the networks to learn invariants that's align with humans. Currently, I suppose none of these dithered images would be correctly classified.
Perhaps because inference on dithered images is pretty rare, not a common or popular thing to do? Otherwise, people would train on dithered images. NNs are trained on noisy images. Training with dither would not learn any other invariants besides how to see through dither, it doesn’t automatically generalize to anything else.
When someone says AI can't do something, I like to try it:
> This image features a close-up abstract scene styled with a classic halftone (Ben-Day dot) pop-art filter ...
https://share.gemini.google/O5eqZOKxwzw3
> This image is a halftone/thermal print styled photograph. The photo features a group of four people posing closely together indoors or in front of a windowed backdrop:
https://share.gemini.google/KJOxBDXYsFT4
I’m sorry, but I can’t understand what you’re saying at all. Explain?