From 476d2fce5ba4cac1de3046cc9701159d5bd61795 Mon Sep 17 00:00:00 2001 From: YunLiu <55491388+KumoLiu@users.noreply.github.com> Date: Tue, 7 Jan 2025 12:59:39 +0800 Subject: [PATCH] Fix broken link for 2d GAN (#1916) Fix broken link for 2d GAN ### Checks - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t ` Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com> --- generation/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/README.md b/generation/README.md index df50ed899..6c4fe96aa 100644 --- a/generation/README.md +++ b/generation/README.md @@ -43,7 +43,7 @@ Example shows the use cases of using MONAI to evaluate the performance of a gene ## [Training a 2D VQ-VAE + Autoregressive Transformers](./2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb): Example shows how to train a Vector-Quantized Variation Autoencoder + Transformers on the MedNIST dataset. -## Training VQ-VAEs and VQ-GANs: [2D VAE](./2d_vqvae/2d_vqvae_tutorial.ipynb), [3D VAE](./3d_vqvae/3d_vqvae_tutorial.ipynb) and [2D GAN](./3d_autoencoderkl/2d_vqgan_tutorial.ipynb) +## Training VQ-VAEs and VQ-GANs: [2D VAE](./2d_vqvae/2d_vqvae_tutorial.ipynb), [3D VAE](./3d_vqvae/3d_vqvae_tutorial.ipynb) and [2D GAN](./2d_vqgan/2d_vqgan_tutorial.ipynb) Examples show how to train Vector Quantized Variation Autoencoder on [2D](./2d_vqvae/2d_vqvae_tutorial.ipynb) and [3D](./3d_vqvae/3d_vqvae_tutorial.ipynb), and how to use the PatchDiscriminator class to train a [VQ-GAN](./2d_vqgan/2d_vqgan_tutorial.ipynb) and improve the quality of the generated images. ## [Training a 2D Denoising Diffusion Probabilistic Model](./2d_ddpm/2d_ddpm_tutorial.ipynb):