Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
TABLE IV: Different Implementations of CLSTM (best results in bold). Details in the text.
['[EMPTY]', 'Test set UA Recall [%] Emotion 2 classes', 'Test set UA Recall [%] Emotion 5 classes', 'Test set UA Recall [%] Age', 'Test set UA Recall [%] Gender', 'Test set UA Recall [%] Average']
We empirically evaluated six different implementations of the CLSTM model, by using the two sequences (h1,…,hn) and (c1,…,cn) of hidden and cell states, each with the three operations: max, mean and using the last. We compared the different implementations of the CLSTM model both on the emotion classification task and the age and gender classification task. For each task we used the same network architecture and training procedure as was used in the above experiments, with the only difference being the implementation of the CLSTM layers (as in the experiments above, the emotion classification model contains two CLSTM layers, and the age and gender classification model contains one CLSTM layer). We observed that in all four tasks, the average results of implementations using the cell states is better than the average results of implementations using the hidden states, but still in one task of the four the implementation yielding the best result used the hidden states. The results regarding which one of the six different implementations is preferable are ambiguous, and we cannot point out one implementation which is better than the others. In addition, comparing the usage of max, mean and the last vector of a sequence also gave ambiguous results, with a slight preference towards using the max operator.