# Channel Decomposition into Painting Actions

## TL;DR

This paper introduces a method to decompose neural network convolutional layers into human-like painting actions, incorporating artistic style variations and object-aware planning for improved interpretability and artistic control.

## Contribution

It presents a novel approach to interpret neural network channels as painting actions driven by human-like stroke parameters and object detection for better planning.

## Key findings

- Effective channel decomposition into painting actions.
- Enhanced artistic style variations through parameter extensions.
- Insights into channel sensitivity and stroke penetration.

## Abstract

This work presents a method to decompose a convolutional layer of the deep neural network into painting actions. To behave like the human painter, these actions are driven by the cost simulating the hand movement, the paint color change, the stroke shape and the stroking style. To help planning, the Mask R-CNN is applied to detect the object areas and decide the painting order. The proposed painting system introduces a variety of extensions in artistic styles, based on the chosen parameters. Further experiments are performed to evaluate the channel penetration and the channel sensitivity on the strokes.