> classification problem with a large number of labels, but only have a small amount of training data for some (or all) of them, it will probably never work
That is true. On the other hand i have seen someone once perform a trick which looked miraculous to me.
We had a classification problem with a small number of labels (~3). And one of the labels had unfortunately way less samples in our training set. Then someone trained a GAN to turn the images of the abundant labels into images of the rare labels. We added those syntetically generated images to the training set and it improved our classification performance as best as we could tell.
That one still feels a bit like black magic to me to be honest. Almost as if we got more out of less with a trick.
That is true. On the other hand i have seen someone once perform a trick which looked miraculous to me.
We had a classification problem with a small number of labels (~3). And one of the labels had unfortunately way less samples in our training set. Then someone trained a GAN to turn the images of the abundant labels into images of the rare labels. We added those syntetically generated images to the training set and it improved our classification performance as best as we could tell.
That one still feels a bit like black magic to me to be honest. Almost as if we got more out of less with a trick.