Coloring Deep CNN Layers with Activation Hue Loss
Louis-François Bouchard Mohsen Ben Lazreg Matthew Toews
This paper proposes a novel hue-like angular parameter to model the structure
of deep convolutional neural network (CNN) activation space, referred to as the
{\em activation hue}, for the purpose of regularizing models for more effective
learning. The activation hue generalizes the notion of color hue angle in
standard 3-channel RGB intensity space to $N$-channel activation space. A
series of observations based on nearest neighbor indexing of activation vectors
with pre-trained networks indicate that class-informative activations are
concentrated about an angle $\theta$ in both the $(x,y)$ image plane and in
multi-channel activation space. A regularization term in the form of hue-like
angular $\theta$ labels is proposed to complement standard one-hot loss.
Training from scratch using combined one-hot + activation hue loss improves
classification performance modestly for a wide variety of classification tasks,
including ImageNet.