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| author | Vasil Zlatanov <v@skozl.com> | 2018-12-10 16:17:22 +0000 |
|---|---|---|
| committer | Vasil Zlatanov <v@skozl.com> | 2018-12-10 16:17:22 +0000 |
| commit | 61a972f93c94f276aeffd4fded902810117d2391 (patch) | |
| tree | a8dbdb2dd778e46b135a915b0563394c2f321726 | |
| parent | cc0ce36fb75f4b207311c07daf86b835aea0a745 (diff) | |
| download | vz215_np1915-61a972f93c94f276aeffd4fded902810117d2391.tar.gz vz215_np1915-61a972f93c94f276aeffd4fded902810117d2391.tar.bz2 vz215_np1915-61a972f93c94f276aeffd4fded902810117d2391.zip | |
Add nice abstract
| -rwxr-xr-x | report2/metadata.yaml | 7 |
1 files changed, 6 insertions, 1 deletions
diff --git a/report2/metadata.yaml b/report2/metadata.yaml index 467efb6..f35d6aa 100755 --- a/report2/metadata.yaml +++ b/report2/metadata.yaml @@ -7,6 +7,11 @@ numbersections: yes lang: en babel-lang: english 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 + 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 $N$. ... |