machine learning - How different does each output unit have to be? -


i'll use example of classifying pumpkins. take example of cinderella pumpkin

enter image description here

versus gourd pumpkin

enter image description here

intuitively, may seem wise classify these images 2 different outputs, cinderella-pumpkin , gourd-pumpkin, due how different look.

my question is, if take training set of images includes both cinderella pumpkins , gourd pumpkins , classify both of them under category of pumpkin, performance of network worse if instead separated them 2 categories? threshold when 2 objects different should put separate categories?

or take more extreme example sake of clarity, if took pictures of cats , pictures of pineapples , classified them under same category, how ability of network affected in classifying each respective object in comparison if 1 created cat output , pineapple output?

it depends on inherent similarity of training observations. don't set threshold: use power iteration clustering (or other unsupervised classification) guide me on there significant divisions in training data. k-means popular choice, since it's common implementation, , relatively easy comprehend.

the other consideration similarity of "non-pumpkin" data, such basketball (compared cinderella). again, take unsupervised learning approach. in case, expect basketball plot closer cinderella either gourd. suggests separate classes pumpkin types -- or perhaps more feature detection in image processing, find similarities across pumpkin varieties.

does help?


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