Instructions to use clementchadebec/reproduced_ciwae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pythae
How to use clementchadebec/reproduced_ciwae with pythae:
from pythae.models import AutoModel model = AutoModel.load_from_hf_hub("clementchadebec/reproduced_ciwae") - Notebooks
- Google Colab
- Kaggle
Commit ·
625e7e6
1
Parent(s): 0a5809d
Update README.md
Browse files
README.md
CHANGED
|
@@ -17,6 +17,6 @@ This trained model reproduces the results of the official implementation of [1].
|
|
| 17 |
|
| 18 |
| Model | Dataset | Metric | Obtained value | Reference value |
|
| 19 |
|:---:|:---:|:---:|:---:|:---:|
|
| 20 |
-
| CIWAE (beta=0.05) | Dyn. Binarized MNIST | NLL (5000 IS) | 84.
|
| 21 |
|
| 22 |
[1] Rainforth, Tom, et al. "Tighter variational bounds are not necessarily better." International Conference on Machine Learning. PMLR, 2018.
|
|
|
|
| 17 |
|
| 18 |
| Model | Dataset | Metric | Obtained value | Reference value |
|
| 19 |
|:---:|:---:|:---:|:---:|:---:|
|
| 20 |
+
| CIWAE (beta=0.05) | Dyn. Binarized MNIST | NLL (5000 IS) | 84.74 (0.01) | 84.57 (0.09) |
|
| 21 |
|
| 22 |
[1] Rainforth, Tom, et al. "Tighter variational bounds are not necessarily better." International Conference on Machine Learning. PMLR, 2018.
|