3 ms·
Yeah but this is an ML 101 view of the thing, so let me put it this way The topology of the (real) input data is part of the dimensionality reduction needed fo
by raverbashing 3y ago
Yeah but this is an ML 101 view of the thing, so let me put it this way
The topology of the (real) input data is part of the dimensionality reduction needed for the identification to work, especially at a given number of parameters
Understanding how the data is connected in the input (there are image lines and they form a "shape") helps a lot and CNNs used this to their advantage
Maybe a very powerful RNN could deduce this by itself, but it is wasted processing.
GPT also uses the input topology to its favour when it uses tokens, process text sequentially, etc
- quickthrower2 3y agoIf this is the case, and I believe you, then this implies: "bytes are NOT all you need". You need a good representation of your image for the network to work well. It might so happen that one of the popular formats, maybe JPEG does a good job. But a dedicated ML person could do better most likely. This is probably what you would classically call "feature engineering"? And this is all hyperparameter search stuff in a way.