A Deep Convolutional Generative Adversarial Network to generate Faces of Anime Characters
Kaggle Notebook Link: https://www.kaggle.com/soumikrakshit/animegan
Dataset Link: https://www.kaggle.com/soumikrakshit/anime-faces
AnimeGAN is based on the standard DCGAN architecture by Alec Radford, Luke Metz, Soumith Chintala. It mainly comprises of convolution layers without max pooling and fully connected layers. It uses convolutional stride and transposed convolution for the downsampling and the upsampling.
The AnimeGAN was trained on 21551 anime face images from size (64, 64) and was trained for 15000 epochs for a batch size of 32 images using Nvidia Tesla K80 GPU in the Kaggle Kernel environment.
