Methods

由 Y Liu 著作 · 2022 · 被引用 11 次 — Abstract:Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, ...

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping. - shaoanlu/faceswap-GAN.

Face generation GAN. Our objective is to create a model capable of generating realistic human images that do not exist in reality. (In future, I will upload a ...

2023年12月21日 — epochs = 1 # In practice, use ~100 epochs gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim) gan.compile ...

由 Y Shen 著作 · 2018 · 被引用 190 次 — Unlike two-player GANs, this work generates identity- preserving faces by proposing FaceID-GAN, which treats a classifier of face identity as the third player, ...

Face generation is the task of generating (or interpolating) new faces from an existing dataset. The state-of-the-art results for this task are located in ...

We take batches of image from the input distribution along with generated fake images and fed it to discriminator so that it learns to distinguish between real ...

Explore and run machine learning code with Kaggle Notebooks | Using data from CelebFaces Attributes (CelebA) Dataset.

The software uses a generative adversarial network (GAN) approach, in which two neural networks play a game of cat and mouse, one attempting to generate ...