# Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2

arXiv: https://arxiv.org/abs/1712.03400 · HTML: https://mlsanity.com/paper/1712.03400/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2017.

## Built on by (most cited first, 7 of 7)

- [Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2108.04547/) (2021)
- [Color-S4L: Self-supervised Semi-supervised Learning with Image Colorization](https://mlsanity.com/paper/2401.03753/) (2024)
- [CCC: Color Classified Colorization](https://mlsanity.com/paper/2403.01476/) (2024)
- [ViT-Inception-GAN for Image Colourising](https://mlsanity.com/paper/2106.06321/) (2021)
- [Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images](https://mlsanity.com/paper/2205.12867/) (2022)
- [FlowChroma -- A Deep Recurrent Neural Network for Video Colorization](https://mlsanity.com/paper/2305.13704/) (2023)
- [CCC++: Optimized Color Classified Colorization with Segment Anything Model (SAM) Empowered Object Selective Color Harmonization](https://mlsanity.com/paper/2403.11494/) (2024)

## Abstract (arXiv)

We review some of the most recent approaches to colorize gray-scale images using deep learning methods. Inspired by these, we propose a model which combines a deep Convolutional Neural Network trained from scratch with high-level features extracted from the Inception-ResNet-v2 pre-trained model. Thanks to its fully convolutional architecture, our encoder-decoder model can process images of any size and aspect ratio. Other than presenting the training results, we assess the "public acceptance" of the generated images by means of a user study. Finally, we present a carousel of applications on different types of images, such as historical photographs.