# Color Constancy Convolutional Autoencoder

arXiv: https://arxiv.org/abs/1906.01340 · HTML: https://mlsanity.com/paper/1906.01340/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Pixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes](https://mlsanity.com/paper/2402.02922/) (2024)
- [Investigating Color Illusions from the Perspective of Computational Color Constancy](https://mlsanity.com/paper/2312.13114/) (2023)
- [Colour alignment for relative colour constancy via non-standard references](https://mlsanity.com/paper/2112.15106/) (2021)
- [Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods](https://mlsanity.com/paper/2604.20243/) (2026)

## Abstract (arXiv)

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.