Comments (3)
Secondly, is it normal for the content preservation loss g_loss_con to be 0.0 for the first few epochs? I'm finding that the encoder basically encodes everything to the same vector in the hidden dimension, hence the loss is 0.0
Yes, it's expected to have the content preservation loss close to 0.0 because we want to preserve the content as much as possible even at the beginning of training. Note that the output from the content encoder is normalized. Therefore, if the training is stabilized, we should have different content codes for different inputs.
from ai-research-code.
Hi,
For first question, we got a question with same error... please check: sony/nnabla-ext-cuda#367
I hope install page or docker container will help you.
For second question, please wait a moment as I will ask developer to check your question.
from ai-research-code.
Thanks, will see how the training goes.
Closing this for now
from ai-research-code.
Related Issues (20)
- Chinese supported? HOT 2
- Adding additional speakers - transfer learning
- [Mixed Precision DNNs]: ImageNet codebase? HOT 1
- 【NVC-Net】RuntimeError: target_specific error in backward_impl. Failed `status == CUDNN_STATUS_SUCCESS`: UNKNOWN HOT 1
- 【NVC-Net】ImportError: libcudart.so.10.2: cannot open shared object file: No such file or directory HOT 6
- 【NVC-Net】Mutli-GPU training multiple models? HOT 2
- No pretrained NVC model HOT 5
- Memory allocation failed HOT 16
- MobileNet implementation for Mixed Precision DNNs
- Segmentation fault and RuntimeError: value error in setup_impl HOT 3
- resuming training from checkpoint HOT 2
- Question about Mixed Precision DNNs HOT 5
- [Quantized Depth Completion] Questions about implementation details HOT 5
- pretrained NVC model HOT 1
- NVCnet g_loss_con=0.0000 while training HOT 2
- [NVC-Net] About 16 kHz training and model convergence HOT 2
- [NVC-NET]Inference in CPU environment HOT 2
- [X-UMX] Bad performance when using --targets HOT 1
- hi,where is tvc-gmm code? HOT 2
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 ai-research-code.