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[Discussion] What are best practices when building/training very small models? (alien.top)

submitted 2 years ago by Snagnar@alien.top to c/machinelearning@academy.garden

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I am currently trying to build small convolutional regression models with very tight constraints regarding model size (max. a few thousand parameters).

Are there any rules of thumb/gold standards/best practices to consider here? E.g. should I prefer depth of the model over width, do skip connections add anything in these small scales, are there any special training hacks that might boost performance, etc?

Any hints or pointers, where to look are greatly appreciated.

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[–] txhwind@alien.top 1 points 2 years ago

if you have a lot of data, you can try to train larger ones first, and solve the model size problem with all kinds of model compression or inference optimization methods.