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cbgm's Introduction

Concept Bottleneck Generative Models

This is the official open source repository for Concept Bottleneck Generative Models

Installation

Dependencies can be installed using the following command:

mamba env create --file env.yaml
conda activate cbgm

Usage

To run a model you need to specifiy the model type and dataset. For example to run a vaegan on celeba training command is as follows:

python main.py -m vaegan -d celeba

While for a vaegan with a concept bottleneck layer is:

python main.py -m cb_vaegan -d celeba

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

cbgm's People

Contributors

ismaia11 avatar everyday847 avatar

Stargazers

Weiyi Q avatar Jonathan Balloch avatar

Watchers

 avatar Stephen Ra avatar

cbgm's Issues

Unable to reproduce VAE-GAN results on CelebA

Hi, congratulations on your great work and acceptance to ICLR.

I was trying to reproduce the baseline VAE-GAN on CelebA but it seems that the results at the end of 50 epochs of training (with the given config file) are not as expected. See below for screenshots of 49_latent.png (generated image from sampled latent) and 49.png (generated image from real latent). There is some kind of mode collapse there, could you please advise on how to fix this?

49_latent
49

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