-
Notifications
You must be signed in to change notification settings - Fork 24
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add some labels from target domain hurts performance #17
Comments
Hi Yiru. I have not tested this yet. Did you try all the domain pairs or simply try one pair? |
Hi,
I only tried modelnet to ShapeNet. It seems off that from UDA to SDA
(supervised domain adaptation), the performance drops 🤔Could you kindly
take a look at my code snippet so see if there’s any obvious error?
Much appreciated!
-Yiru
…On Fri, Oct 16, 2020 at 6:49 AM canqin001 ***@***.***> wrote:
Hi Yiru. I have not tested this yet. Did you try all the domain pairs or
simply try one pair?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<#17 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/ADMGPCDVBP357CCMW74QPKTSLBFO5ANCNFSM4SSYRRDQ>
.
|
Hi Yiru. I don't see any bugs in your code. For the SDA setting, I guess the model might be overfitting towards the target domain if few labeled samples are provided. It is a very interesting problem. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Hi there,
First of all, nice work!
I'd like to add some labels from target domain for training (pick 100 shapes from target domain to assign the GT labels for training). However, it turns out the classification accuracy drops from ~64% to ~59%. Below is my code snippet:
What I don't quite understand is that -- why the performance will drop after adding some real labels from target domain for training? Do you have any insights into this?
Much appreciate your comments!
Best,
Yiru
The text was updated successfully, but these errors were encountered: