add note for VaDE

Author: eelxpeng

README.md

@@ -13,4 +13,10 @@ Implementation of Variational Deep Embedding from the IJCAI2017 paper:
 
 Jiang, Zhuxi, et al. "Variational deep embedding: An unsupervised and generative approach to clustering." International Joint Conference on Artificial Intelligence. 2017.
 
-The original code is written in [Keras](https://github.com/slim1017/VaDE). However, the original code is incorrect when computing the loss function. And I have corrected the loss function part with my code. The example usage can be found in `test/test_vade-3layer.py`, and it uses the pretrained weights from autoencoder in `test/model/pretrained_vade-3layer.pt`. Note: the pretrained weights is important to initialize the weights of VaDE.
+The original code is written in [Keras](https://github.com/slim1017/VaDE). However, the original code is incorrect when computing the loss function. And I have corrected the loss function part with my code. The example usage can be found in `test/test_vade-3layer.py`, and it uses the pretrained weights from autoencoder in `test/model/pretrained_vade-3layer.pt`. 
+
+Note: 
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+* The pretrained weights is important to initialize the weights of VaDE.
+* Unlike the original code using combined training and test data for training and evaluation, I split the training and test data, and only use training data for training and test data for evaluation. I think it is a more appropriate way to evaluate the method for generalization.
+* As found, with above evaluation scheme and training for 3000 epochs, the clustering accuracy achieved is 93.65\%.