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Image synthesis has been a trending task for the AI community in recent years. Many works have shown the potential of Generative Adversarial Networks (GANs) to deal with tasks such as text or audio to image synthesis. In particular, recent advances in deep learning using audio have inspired many works involving both visual and auditory information. In this work we propose a face synthesis method which is trained end-to-end using audio and/or language representations as inputs. We used this project as baseline.
This implementation currently only support running with GPUs.
`python runtime.py
Arguments:
type : GAN archiecture to use (gan | wgan | vanilla_gan | vanilla_wgan). default = gan. Vanilla mean not conditionaldataset: Dataset to use (birds | flowers). default = flowerssplit : An integer indicating which split to use (0 : train | 1: valid | 2: test). default = 0lr : The learning rate. default = 0.0002diter : Only for WGAN, number of iteration for discriminator for each iteration of the generator. default = 5vis_screen : The visdom env name for visualization. default = gansave_path : Path for saving the models.l1_coef : L1 loss coefficient in the generator loss fucntion for gan and vanilla_gan. default=50l2_coef : Feature matching coefficient in the generator loss fucntion for gan and vanilla_gan. default=100pre_trained_disc : Discriminator pre-tranined model path used for intializing training.pre_trained_gen Generator pre-tranined model path used for intializing training.batch_size: Batch size. default= 64num_workers: Number of dataloader workers used for fetching data. default = 8epochs : Number of training epochs. default=200cls: Boolean flag to whether train with cls algorithms or not. default=False[1] Generative Adversarial Text-to-Image Synthesis https://arxiv.org/abs/1605.05396
[2] Improved Techniques for Training GANs https://arxiv.org/abs/1606.03498
[3] Wasserstein GAN https://arxiv.org/abs/1701.07875
[4] Improved Training of Wasserstein GANs https://arxiv.org/pdf/1704.00028.pdf