# Coloring Deep CNN Layers with Activation Hue Loss

Source: [Coloring Deep CNN Layers with Activation Hue Loss](<https://www.sota2.com/research/paper/68fa70d11e209131cea4d6b5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:50:52.513Z

Authors: Louis-Fran\\c{c}ois Bouchard, Mohsen Ben Lazreg, Matthew Toews

Published: 2023-10-05T21:30:37.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.03911>)

[PDF](<https://arxiv.org/pdf/2310.03911.pdf>)