id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ied43mahmvguhklfdeysxshe5m	Edoardo Lisi	Modelling and forecasting art movements with CGANs	2020	14	.pdf	application/pdf	8319	1171	67	namely Post-Minimalism, by comparing the 'future' art we generate with our method to PostMinimalist art (which was not part of our training set) and other recent movements.2 CGANs generate new samples from the conditional distribution of the data X given the latent we use a CGAN to generate images based upon the prediction given by the VAR model in the latent — Generate new images that have latent representations corresponding to the (K + 1)th category. There exist some methods that have used GANs and/or autoencoders for predicting new art movements. We now describe the general method used to model a sequence of latent structures of images and use this is trained on the whole space of the K movements, new samples can be generated from an individual standard autoencoder is used for both latent modelling and generation of new images. standard autoencoder is used both for modelling the latent space and for generating images	./cache/work_ied43mahmvguhklfdeysxshe5m.pdf	./txt/work_ied43mahmvguhklfdeysxshe5m.txt