GAN, DCGAN, CGAN, Pix2Pix, CycleGANに引き続きGAN手法のお勉強。 https://blog.negativemind.com/2020/03/30/cyclegan-image-to-image-translation-by-learning-some-relationship-between-the-domains/ https://blog.negativemind.com/portfolio/generative-adversarial-network/ 次はPGGAN (Progressive Growing GAN)について。

PGGAN (Progressive Growing GAN)

PGGAN (Progressive Growing GAN)はICLR 2018で発表された研究 Progressive growing of GANs…

Recent advances in generative adversarial networks have made it possible to feed a neural network a dataset of images (e.g. photos of faces) and generate images that look like they belong to the dataset, but don’t.

The Deep Convolutional Generative Adversarial Network (DCGAN) paper outlines a technique for generating medium-sized images (32x32 or 64x64 pixels, usually). Like other GAN architectures, DCGAN trains two neural networks in tandem — one network generates images (it starts, initially, by producing random noise) and another network tries to tell real images from fake ones. As this discriminator improves, the generator is trained to fool it by back propagating through the discriminator. Ideally, as the generator gets good at fooling the discriminator, the discriminator improves, forcing the generator to produce ever higher-quality fakes.

I trained a modified DCGAN architecture on the CelebA dataset — mine diverges from DCGAN as it uses average-pooling in the discriminator, instead of strided convolutions, but the effect is the same.

After a lot of hyperparameter tuning (GANs are notoriously difficult to train in a stable way), I got it to work! After a couple hours, I was generating images that looked like this:

Clearly fakes, but clearly doing a decent job of emulating the real dataset.

Since the generator takes in  random noise vector as a seed for the generated image, it’s possible to explore the latent space by feeding in latent vectors that vary only a little bit. I created a GIF by interpolating the latent vector and capturing the generator’s output: