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I'm trying to train a seq2seq model that for every timestep in a given timeseries sample will output 1 of 6 possible labels.

Furthermore, the training data is constructed in such a way that

  • Each sample can only contain a maximum of 2 types of labels

  • Label 1 can co-occur with any other label. No other labels can co-occur.

Is there any way to specifically guide training with rules like these, or is my best bet to hope that the model will pick up on this? In roughly 85-90% of cases the model behaves as hoped, but in a few noisy edge-cases it sometimes oscillates wildly between different labels, throughout the sample.

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    $\begingroup$ Simply remap your labels into 6 + 5 + 1=12 new labels: empty, 1-6, 12, 13, 14, 15, 16. $\endgroup$
    – Valentas
    Apr 25, 2019 at 12:02
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    $\begingroup$ Could you do a more detailed writeup? I don't really understand what you're getting at. Edit: do you mean "12" as in "label 2 if it co-occurs with 1"? $\endgroup$ Apr 25, 2019 at 12:17

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