1 research outputs found
Measuring and improving the quality of visual explanations
The ability of to explain neural network decisions goes hand in hand with
their safe deployment. Several methods have been proposed to highlight features
important for a given network decision. However, there is no consensus on how
to measure effectiveness of these methods. We propose a new procedure for
evaluating explanations. We use it to investigate visual explanations extracted
from a range of possible sources in a neural network. We quantify the benefit
of combining these sources and challenge a recent appeal for taking bias
parameters into account. We support our conclusions with a general assessment
of the impact of bias parameters in ImageNet classifier