@conference {cTarres,
	title = {GAN-based Image Colourisation with Feature Reconstruction Loss},
	booktitle = {CVPR 2021 Women in Computer Vision Workshop},
	year = {2021},
	month = {06/2021},
	address = {Virtual},
	abstract = {<p>Image colourisation is the task of adding plausible colour to grayscale images. This transformation requires obtaining a three dimensional colour-valued mapping from a real-valued grayscale image, which leads to an undetermined problem because the gray-scale semantics and texture provide cues for multiple possible colour mappings. The goal of image colourisation in not to recover the ground truth colour in a manner that it is perceived as natural by a human observer.\&nbsp;<span style="font-size: 13.008px;">Our work takes as a baseline a scheme based on an end-to-end trainable convolutional neural network (CNN) trained with a smooth L1 loss to predict the $ab$ channels of a colour image given the $L$ channel. We introduce an extra perceptual reconstruction loss during training to improve the capabilities of a adversarial adversarial model, that we adopt as a baseline.</span></p>},
	author = {Laia Tarr{\'e}s and G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Mrak, Marta}
}
