CPGAN :  An Efficient Architecture Designing for Text-to-Image Generative Adversarial Networks Based on Canonical Polyadic Decomposition

Algorithm 1

Overall scheme of CPGAN algorithm.

	Input: mini-batch images , text description , and number of training batch steps .
	Output: CPGAN model.
(1)	Use equations (5)–(7) to decompose the original convolutional layer in generator;
(2)	Add CA module for text embedding and add decoders layers;
(3)	Select an appropriate learning rate for the decomposed model;
(4)	Fortodo
(5)	Encode text description into embedding ;
(6)	Feed into CA and obtain ;
(7)	Sample from and random noise ;
(8)	Concatenate and and feed it into the generator;
(9)	Update discriminator D by equation (11);
(10)	Update generator G by equation (10);
(11)	End for
(12)	Discard all decoders and get a trained CPGAN.