The Long-Short Story of Movie Description
Table 4: Ensembles of networks with different random initializations. All results reported on the validation set of MPII-MD.
['Approach', '[ITALIC] METEOR']
In most of experiments we trained our networks for 25,000 iterations. After looking at the METEOR performance for intermediate iterations we found that for the step size 4000 at iteration 15,000 we achieve best performance overall. Additionally we train multiple LSTMs with different random orderings of the training data. In our experiments we combine three in an ensemble, averaging the resulting word predictions. To summarize, the most important aspects that decrease over-fitting and lead to a better sentence generation are: (a) a correct learning rate and step size, (b) dropout after the LSTM layer, (c) choosing the training iteration based on METEOR score as opposed to only looking at the LSTM accuracy/loss which can be misleading, and (d) building ensembles of multiple networks with different random initializations.