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| -rw-r--r-- | report/bibliography.bib | 8 | ||||
| -rw-r--r-- | report/paper.md | 4 |
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}[] |