Master's Thesis. Master's in Data Science at Universitat Oberta de Catalunya.
- Adrián Arnaiz Rodríguez - [email protected]
- Dr. Baris Kanber - [email protected]
-
With Data Augmentation:
- 2 experiments (MSE and DSSIM Loss) for each of the following architectures:
- Shallow residual autoencoder (full-pre)
- Shallow residual autoencoder (full-pre) + L2 reg.
- Skip connection autoencoder
- Skip connection autoencoder + L2 reg.
- Myronenko Autoencoder
- RESIDUAL-UNET (proposed new improved architecture)
- 2 experiments (MSE and DSSIM Loss) for each of the following architectures:
-
Without Data Augmentation:
- MSE Loss
- Shallow residual autoencoder (original)
- Shallow residual autoencoder (full-pre)
- Shallow residual autoencoder (full-pre) + L2 reg.
- Skip connection autoencoder
- Myronenko Autoencoder
- Myronenko Autoencoder + L2 reg.
- MSE Loss
| Model | loss | L2 | Val loss | MSE | DSSIM | PSNR |
|---|---|---|---|---|---|---|
| Residual U-NET | MSE | No | 3.58e-05 | 3.44e-05 | 2.95e-03 | 44.9 |
| Shallow RES full-pre | MSE | No | 1.55e-04 | 1.51e-04 | 6.75e-03 | 38.6 |
| Skip connection CAE | MSE | Yes | 2.69e-04 | 2.25e-04 | 1.65e-02 | 36.8 |
| Skip connection CAE | MSE | No | 3.10e-04 | 2.99e-04 | 9.36e-03 | 35.7 |
| Myronenko CAE | MSE | No | 3.38e-04 | 3.27e-04 | 1.57e-02 | 35.1 |
| Shallow RES full-pre | MSE | Yes | 3.72e-04 | 3.24e-04 | 1.14e-02 | 35.2 |
| Residual U-NET | DSSIM | No | 1.50e-03 | 7.49e-05 | 1.44e-03 | 41.8 |
| Shallow RES full-pre | DSSIM | Yes | 4.42e-03 | 2.34e-04 | 3.70e-03 | 36.7 |
| Shallow RES full-pre | DSSIM | No | 4.19e-03 | 2.88e-04 | 4.14e-03 | 35.9 |
| Myronenko CAE | DSSIM | No | 4.39e-03 | 6.69e-04 | 4.31e-03 | 32.1 |
| Skip connection CAE | DSSIM | Yes | 4.82e-03 | 4.08e-04 | 4.38e-03 | 34.2 |
| Skip connection CAE | DSSIM | No | 4.90e-03 | 4.57e-04 | 4.71e-03 | 33.7 |
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