[D] performance of dropout in RNN.

The question is, why does applying dropout to RNN such as GRU, LSTM, BiGRU, BiLSTM don't produce performance well as in the computer vision domain?

I have done a variety of experiments for this in the layer RNN or Dense. But the most useful value was only 0, which means non-using dropout is the best option.

It depends on what kind of time series problem, but it is curious about why the approach doesn't create any good results in the …

computer computer vision dropout good gru kind lstm machinelearning performance rnn series time series value vision