Deep convolutional neural networks have achieved state-of-art-performance on many vision-related tasks. In this work we investigate how color information is utilized by detecting learned color-sensitive features.
Deep CNNs (such as VGG1 and Alexnet2) exhibit different performance on classification, depending on whether they are presented with color or grayscale images.
Color-sensitive units are defined as units whose average activation changes significantly between color and grayscale versions of the same data. In the figure below, we show color-sensitivity based on images from the PASCAL VOC3 dataset.
The hue-specificity of units is measured by observing the activation value in response to a monochrome image of varying hue. The co-activation between units and classes is used to identify units that are class-invariant, or class-specific.
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