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.
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.
I am not sure that's true. It would learn to not focus in texture which is what current nns do.
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
This is a good way to think.
I’m sorry, but I can’t understand what you’re saying at all. Explain?
There are many features in an image dataset which a can correctly classify the dataset. Neural nets usually focus in texture which we human clearly don't, we focus on a higher level abstraction and usually ignore texture. Using either would encourage the network to use broader features and ignore texture