Convolutional RNN
저자: pallav learn
1. Introduction
2. Conclusion
3. Discussion
4. Abstract
5. Related work
6. Experiments
6.1. Emotion classification
6.2. Age and gender classification
6.3. Didn't use backpropogation through time (BPTT)
7. CLSTM Architecutre
7.1. CLSTM
7.2. CBLSTM
7.3. Extended CLSTM
7.4. Max, mean, last pooling
7.5. Use hidden state, cell state, output as extracted feature
8. Evaluation metric.
8.1. Unweighted average (UA) recall
9. Convolutional LSTM
10. Extracting features
11. LSTM & bi-directional LSTM
12. Convolution before RNN
13. Convolutional RNN
