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-rw-r--r--report/bibliography.bib8
-rw-r--r--report/paper.md4
2 files changed, 11 insertions, 1 deletions
diff --git a/report/bibliography.bib b/report/bibliography.bib
index 3ccece5..430d8b5 100644
--- a/report/bibliography.bib
+++ b/report/bibliography.bib
@@ -1,3 +1,11 @@
+@INPROCEEDINGS{lenet,
+ author = {Yann Lecun and Léon Bottou and Yoshua Bengio and Patrick Haffner},
+ title = {Gradient-based learning applied to document recognition},
+ booktitle = {Proceedings of the IEEE},
+ year = {1998},
+ pages = {2278--2324}
+}
+
@misc{improved,
Author = {Tim Salimans and Ian Goodfellow and Wojciech Zaremba and Vicki Cheung and Alec Radford and Xi Chen},
Title = {Improved Techniques for Training GANs},
diff --git a/report/paper.md b/report/paper.md
index 522eaed..1989472 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -126,11 +126,13 @@ We evaluate permutations of the architecture involving:
### Inception Score
-Inception score is calculated as introduced by Tim Salimans et. al [@improved]. However as we are evaluating MNIST, we use LeNet as the basis of the inceptioen score.
+Inception score is calculated as introduced by Tim Salimans et. al [@improved]. However as we are evaluating MNIST, we use LeNet-5 [@lenet] as the basis of the inceptioen score.
We use the logits extracted from LeNet:
$$ \textrm{IS}(x) = \exp(\mathbb{E}_x \left( \textrm{KL} ( p(y\mid x) \| p(y) ) \right) ) $$
+We further report the classification accuracy as found with LeNet.
+
### Classifier Architecture Used
\begin{table}[]