Once you get the embeddings from the pretrained model, what classification method should one use for the final classification? Random forest? SVM?
I will also look into the average method you mentioned. Are you saying taking the averages of the embeddings for each class, and then to classify an embedding, see which class average is closest to the embedding (by closest you mean something like the L2 norm)?
It’s encouraging that one can do this in a day, but I haven’t done any ML work for a few years. Should I use Pytorch or Tensorflow?
Thanks.
Once you get the embeddings from the pretrained model, what classification method should one use for the final classification? Random forest? SVM?
I will also look into the average method you mentioned. Are you saying taking the averages of the embeddings for each class, and then to classify an embedding, see which class average is closest to the embedding (by closest you mean something like the L2 norm)?
It’s encouraging that one can do this in a day, but I haven’t done any ML work for a few years. Should I use Pytorch or Tensorflow?
Thanks