# Color Constancy Convolutional Autoencoder

## TL;DR

This paper explores pre-training methods using convolutional autoencoders to improve color constancy, addressing data scarcity and overfitting, and achieving competitive results with fewer parameters.

## Contribution

Introduces two novel pre-training approaches based on convolutional autoencoders for color constancy, including an unsupervised and a semi-supervised method with a new composite-loss function.

## Key findings

- Achieves competitive results with fewer parameters.
- Addresses data scarcity in color constancy datasets.
- Studies overfitting on diverse camera datasets.

## Abstract

In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.