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authornunzip <np.scarh@gmail.com>2018-12-10 17:45:53 +0000
committernunzip <np.scarh@gmail.com>2018-12-10 17:45:53 +0000
commit30583c4ce19bc77e48810894b277857429fbc201 (patch)
tree4099d18333de6958ca87bef27f7e0bcbbf41d336 /report2/metadata.yaml
parented44a6f432cf9e1051edd58e146a54124345adcd (diff)
parent2a5c62f9ea50971ba25c3e8f519e224093ec0090 (diff)
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Merge branch 'master' of git.skozl.com:e4-pattern
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diff --git a/report2/metadata.yaml b/report2/metadata.yaml
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@@ -10,7 +10,7 @@ abstract: |
This report analyses distance metrics learning techniques with regards to
identification accuracy for the dataset CUHK03. The baseline method used for
identification is Eucdidian based Nearest Neighbors based on Euclidean distance.
- The improved approach we propose utilises Jaccardian metrics to rearrange the NN
+ The improved approach evaluated utilises Jaccardian metrics to rearrange the NN
ranklist based on reciprocal neighbours. While this approach is more complex and introduced new hyperparameter, significant accuracy improvements are observed -
approximately 10% increased Top-1 identifications, and good improvements for Top-$N$ accuracy with low $N$.
...