-
I am trying to implement a denoising LSTM based outlier detection method.
my dataset consists of 730 rows of samples, each sample contains 128 values.
This is my code so far:
Building the model h…
-
Hi, Xu. I'm now working on sparse coding image denoising, however, I find that in your program, Par.lambda1 is always set to 0, which means you actually don't use ADMM to find the sulotion. So the fun…
ghost updated
5 years ago
-
### Is there an existing issue for this?
- [X] I have searched the existing issues
### Contact Details
_No response_
### What should this feature add?
Add an ability to pick model as a refiner/ s…
aolko updated
6 months ago
-
### Is there an existing issue for this?
- [X] I have searched the existing issues and checked the recent builds/commits
### What happened?
This only happens with inpainting with masks and not img2…
-
The regular expression to grab urls in the "project_url" field assumes that the URL is the last thing in that field.
This behaviour breaks the URL for the "denoising fMRI" project.
I am going to…
-
- two different models, one for the open sea and one for near land areas (harder)
- problem with ice versus wind and rain areas, can we know when they occur from the meteorology?
- can we learn the …
-
### Checklist
- [X] The issue exists after disabling all extensions
- [x] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a …
-
Recently, 454 released an update to its 454 machines that allows for a random flow order
(Roche v2.8 software with flow Pattern B). SFF files produced using this version will result in gibberish after…
-
I have single shell (one b0 and 30 b1 volumes) data with low b-value (b1=1000). I applied common pre-processing steps (denoising, preproc and bias correction). I estimated the response function of ea…
-
[Fooocus-MRE] is real top.
I switched from A1111 completely.
Last update of A1111 works with refiner in a very bad way, switching between model takes more time than rendering itself.
What I missed …