Described results of Bayesian SSD without dropout
Signed-off-by: Jim Martens <github@2martens.de>
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@ -652,7 +652,9 @@ Bayesian SSD was run with 0.2 confidence threshold and compared
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to vanilla SSD with 0.2 confidence threshold. Coupled with the
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entropy threshold, this comparison shows how uncertain the network
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is. If it is very certain the dropout sampling should have no
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significant impact on the result.
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significant impact on the result. Furthermore, in two cases the
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dropout was turned off to isolate the impact of non-maximum suppression
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on the result.
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Both, vanilla SSD with entropy thresholding and Bayesian SSD with
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entropy thresholding, were tested for entropy thresholds ranging
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@ -708,6 +710,15 @@ not very uncertain. The best performing entropy threshold is not any better than
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the corresponding vanilla SSD without entropy threshold. Therefore, in this
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case the per-class confidence score is far more important for the result.
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The results for Bayesian SSD show a massive impact of the existance of
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non-maximum suppression: maximum \(F_1\) score of 0.371 (with NMS) to 0.006 (without NMS)
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with micro averaging and 0.363 (with NMS) to 0.006 (without NMS) with macro averaging.
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Dropout was disabled in both cases, making them effectively a vanilla SSD run
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with multiple forward passes. Therefore, the low number of open set errors with
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micro averaging (164 without NMS) does not qualify as a good result and is not
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marked bold, although it is the lowest number.
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\begin{table}[ht]
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\begin{tabular}{rcccc}
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\hline
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