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+# DCGAN
+
+## DCGAN Architecture description
+
+## Tests on MNIST
+
+Try some **different architectures, hyper-parameters**, and, if necessary, the aspects of **virtual batch
+normalization**, balancing G and D.
+Please discuss, with results, what challenge and how they are specifically addressing, including
+the quality of generated images and, also, the **mode collapse**. 
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/error_depth_kmean100.pdf}
+\caption{K-means Classification error varying tree depth (left) and forest size (right)}
+\label{fig:km-tree-param}
+\end{center}
+\end{figure}
+
+# CGAN
+
+## CGAN Architecture description
+
+## Tests on MNIST
+
+Try **different architectures, hyper-parameters**, and, if necessary, the aspects of **one-sided label
+smoothing**, **virtual batch normalization**, balancing G and D.
+Please perform qualitative analyses on the generated images, and discuss, with results, what
+challenge and how they are specifically addressing. Is there the **mode collapse issue?**
+
+# Inception Score
+
+## Classifier Architecture Used
+
+## Results
+
+Measure the inception scores i.e. we use the class labels to
+generate images in CGAN and compare them with the predicted labels of the generated images.
+
+Also report the recognition accuracies on the
+MNIST real testing set (10K), in comparison to the inception scores.
+
+**Please measure and discuss the inception scores for the different hyper-parameters/tricks and/or
+architectures in Q2.**
+
+# Re-training the handwritten digit classifier
+
+## Results
+
+Retrain with different portions and test BOTH fake and real queries. Please **vary** the portions
+of the real training and synthetic images, e.g. 10%, 20%, 50%, and 100%, of each.
+
+## Adapted Training Strategy
+
+*Using even a small number of real samples per class would already give a high recognition rate,
+which is difficult to improve. Use few real samples per class, and, plenty generated images in a
+good quality and see if the testing accuracy can be improved or not, over the model trained using
+the few real samples only.
+Did you have to change the strategy in training the classification network in order to improve the
+testing accuracy? For example, use synthetic data to initialise the network parameters followed
+by fine tuning the parameters with real data set. Or using realistic synthetic data based on the
+confidence score from the classification network pre-trained on real data. If yes, please then
+specify your training strategy in details.
+Analyse and discuss the outcome of the experimental result.*
+
+# Bonus
+
+This is an open question. Do you have any other ideas to improve GANs or
+have more insightful and comparative evaluations of GANs? Ideas are not limited. For instance,
+
+\begin{itemize}
+
+\item How do you compare GAN with PCA? We leant PCA as another generative model in the
+Pattern Recognition module (EE468/EE9SO29/EE9CS729). Strengths/weaknesses?
+
+\item Take the pre-trained classification network using 100% real training examples and use it
+to extract the penultimate layer’s activations (embeddings) of 100 randomly sampled real
+test examples and 100 randomly sampled synthetic examples from all the digits i.e. 0-9.
+Use an embedding method e.g. t-sne [1] or PCA, to project them to a 2D subspace and
+plot them. Explain what kind of patterns do you observe between the digits on real and
+synthetic data. Also plot the distribution of confidence scores on these real and synthetic
+sub-sampled examples by the classification network trained on 100% real data on two
+separate graphs. Explain the trends in the graphs.
+
+\item Can we add a classification loss (using the pre-trained classifier) to CGAN, and see if this
+improve? The classification loss would help the generated images maintain the class 
+labels, i.e. improving the inception score. What would be the respective network
+architecture and loss function? 
+
+\end{itemize}
+
+# References
+
+<div id="refs"></div>
+
+\newpage
+
+# Appendix 
+
+