<p>You can determine which part of the image are 'important' for the classification by creating a <strong>class activation map</strong>. Have a look at <a href="https://github.com/ramprs/grad-cam/" rel="nofollow noreferrer">Grad-CAM: Gradient-weighted Class Activation Mapping</a> to see an implementation on github.</p>
<p>Review this paper, <a href="https://arxiv.org/pdf/1311.2901.pdf" rel="nofollow noreferrer">Visualizing and Understanding Convolutional Networks</a>, which strives to understand why a particular large CNN might perform well and how to improve it.</p>
Is there a way to measure which parts of the image are hard for a classifier?
<p>Is there a way to measure how hard an image is to classify (instance hardness), and also a way to measure which parts of the image are hard?</p>
<p>I am currently exploring CNNs and the question of why some images are more difficult to classify than others. In general, it can be said that class overlap is the decisive factor. But now I was wondering whether this can also be quantified concretely in an image regarding different parts/segments/patches of an image and thus someone can determine which parts of the image are so difficult for a classifier?</p>