Comments (8)
Hi @TomatoBoy90 ,
We used the default procedure used in InterFaceGAN. If I remember correctly, this means we used 500,000 randomly sampled w vectors. We then took the 10,000 samples that got the highest attribute score to be the positive samples and 10,000 samples with the lowest scores to be our negative samples.
from encoder4editing.
Hi @TomatoBoy90!
To obtain new editing directions, you should follow the official InterFaceGAN repository's guidelines, in this this issue we wrote how we obtained our 3 editing directions so feel free to check it out (note that you need to use an attribute classification network to label each style vector).
The boundary files from the InterFaceGAN repository were obtained for the pretrained StyleGAN1's latent space, while we trained the boundaries for the pretrained StyleGAN2 (hence the difference you are experiencing).
from encoder4editing.
thank you for your reply quickly.I read InterFaceGAN repository,but seem it need to train svm model,as a supervised learning, it needs label,how to get the label?
from encoder4editing.
Training a SVM model, but how does the machine know which face attribute we need to train?
from encoder4editing.
We used a pretrained network to label each StyleGAN image.
For example, after sampling a style vector w, we generated the corresponding image I=Generator(w) and used a pretrained age classification network to obtain the label age_I = AGE_NET(I) to train the SVM for the age direction.
from encoder4editing.
We used a pretrained network to label each StyleGAN image.
For example, after sampling a style vector w, we generated the corresponding image I=Generator(w) and used a pretrained age classification network to obtain the label age_I = AGE_NET(I) to train the SVM for the age direction.
thanks,how many samples should I take for style vector w? How many samples did you take it?
from encoder4editing.
thank you reply so detailed and generous.Wish you a happy life
from encoder4editing.
Good luck with your experiments!
I am closing the issue :)
from encoder4editing.
Related Issues (20)
- is it possible to fine tune? HOT 2
- Resume training for cars
- How much time did it take to train on FFHQ? HOT 5
- Inference with sample size >1 fails HOT 1
- How to get figure 2 HOT 1
- Error while running inference.py HOT 2
- Training e4e for 512*256 stylegan HOT 2
- Regarding finding directions in W+ space
- Is released ffhq e4e model trained by inversion task? HOT 1
- whether the code is wrong?
- Stuck on Iteration_0
- Model parameters at other resolutions HOT 2
- How do I train an encoder with a resolution of 256*256 on the FFHQ dataset?
- There is a problem with the pre-training weights
- Can I train an encoder using your e4e frame work on a StyleGAN2-ADA pretrained on my own dataset?
- Can I use my own pretrained StyleGAN2-ADA? HOT 1
- Is it possible to get more details in the images?
- is it possible to run this code for gender swap operations ?
- Is it possible to train the encoder which is segmap to face?
- Suggestions on training an encoder on FFHQ-256
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from encoder4editing.