how to test an input with unknown class?

[Greenleaf88]

We train the network to make it classify a sample with unknown class, however, the network need label itself as part of input, In real applications, only those samples with unknown labels need to be classified, if the label is known, the classification becomes unnecenssary, then how to set the label part in the input if the label is unknown?

[buaa-luzhi]

@Greenleaf88 i have the same question

[sheetalreddy]

@Greenleaf88 : Hi, I had the same doubt but the only solution I found was to give the test sample at the end of each episode as the system aims at modelling the predictive distribution p(yt|xt, D1:t−1; θ)

[tristandeleu]

More than simply one-shot learning, the setup for this model is what is called meta learning. One good visualization of how meta learning works is Figure 1 of this paper.

If I understand your question correctly, for each meta training set (ie. for each episode here), you want to only make a prediction for the elements in the "test" part of the set (D_test in the figure). Unfortunately, as far as I know, you can't do that with this model. There is no definite separation between D_train and D_test in an episode. As @sheetalreddy suggested, you could have extra test samples (corresponding to D_test) at the end of your episodes (which now corresponds to the whole D_train) to work around this limitation.