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| author | nunzip <np.scarh@gmail.com> | 2019-03-10 17:02:07 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-03-10 17:02:07 +0000 |
| commit | 8590fd558a5957f193c60715de21433f0c0843a6 (patch) | |
| tree | 14e68d06613377c70cf817d620460ae7d115fb4c /report | |
| parent | 47e6ea316baeba86c6df12634ffbeab2a1da8b73 (diff) | |
| download | e4-gan-8590fd558a5957f193c60715de21433f0c0843a6.tar.gz e4-gan-8590fd558a5957f193c60715de21433f0c0843a6.tar.bz2 e4-gan-8590fd558a5957f193c60715de21433f0c0843a6.zip | |
grammar mistake correction
Diffstat (limited to 'report')
| -rw-r--r-- | report/paper.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/report/paper.md b/report/paper.md index d058051..35d8ba7 100644 --- a/report/paper.md +++ b/report/paper.md @@ -191,7 +191,7 @@ As observed in figure \ref{fig:mix1} we performed two experiments for performanc \end{figure} Both experiments show that an optimal amount of data to boost testing accuracy on the original MNIST dataset is around 30% generated data as in both cases we observe -an increase in accuracy by around 0.3%. In absence of original data the testing accuracy drops significantly to around 20% in both cases. +an increase in accuracy by around 0.3%. In absence of original data the testing accuracy drops significantly to around 20% for both cases. ## Adapted Training Strategy |