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This work analyzes the BigColor method, a fully automatic colorization approach that aims to meet the challenge of providing realistic and vivid colorization for complex and diverse images in real-world scenarios. The method is a BigGAN-inspired encoder-generator network, using a spatial feature map, enabling single forward-pass colorization, supporting arbitrary input resolutions, and producing multimodal colorization results. We provide a short analysis of the method's results and highlight some limitations alongside its achievements. **This is an MLBriefs article, the source code has not been reviewed!** **The original source code is [available here|https://github.com/KIMGEONUNG/BigColor] (last checked 2024/05/23).**
Garcia et al. (Fri,) studied this question.
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