ricvolpi / adversarial-feature-augmentation Goto Github PK
View Code? Open in Web Editor NEWCode for the paper "Adversarial Feature Augmentation for Unsupervised Domain Adaptation", CVPR 2018
License: MIT License
Code for the paper "Adversarial Feature Augmentation for Unsupervised Domain Adaptation", CVPR 2018
License: MIT License
Hi there, I came across your work and was truly impressed. However could your update a Pytorch version of this work? Cause it is so hard for me to understand the TF 1.3.0 version and the new python version do not support the TF 1.3.0 anymore.
when I train the generator of step 1. I find that the discriminator is powerful .the g_loss will be 1 and the d_loss will be 0. How could I do
Thanks for your nice paper and works.
I have some questions about contents of paper and implementation.
I don`t understand that why to use Feature generator that generates features that are similar to original source features instead of source features.
How can I draw t-SNE plots after make features using your code(with train_feature_extractor function)
Can you tell me where`s the reference codes or yours?
thanks!
It's a nice work of DIFA.
Recently, I need to evaluate my own DA model on NYUD cropped datasets. And I follow the description of Tzeng[1] and tried to crop object in RGB and HHA images. However, the number of images in each category is not match to the discription of NYUD in [1] and experiments part of DIFA. Could you give me some suggests about the NYUD?
Many thanks.
[1]Tzeng, Eric, Judy Hoffman, Kate Saenko and Trevor Darrell. “Adversarial Discriminative Domain Adaptation.” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017): 2962-2971.
Thank you for your work!
I'd like to know if it's possible to apply this setting to other domain adaptation datasets, say Office-31 or ImageCLEF. Given that the training images are larger (256x256 etc.), how should I implement those feature extractor, generator etc.
Many thanks.
I trained a encoder
That is to say the above results is in contradiction with the results in the paper. But I have not found any bugs in my code, I want to learn that have you ever got such results.
Thanks!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.