Does this work if the training is on miniimagenet or omniglot and test on customer dataset? I wonder how it "learn to compare" in this situation. Many implementation has use miniimagenet or omniglot the demonstrate the concept of FSL but they use the same for testing (with new classes). I wonder what happened if the new classes comes from total different dataset with different intensify/feature distribution.
Does this work if the training is on miniimagenet or omniglot and test on customer dataset? I wonder how it "learn to compare" in this situation. Many implementation has use miniimagenet or omniglot the demonstrate the concept of FSL but they use the same for testing (with new classes). I wonder what happened if the new classes comes from total different dataset with different intensify/feature distribution.