SPTVMod - Detailed Results

This page presents the full results for Experiment 2, which assesses the impact of mode seeking regularization and the efficacy of the fine-tuning phase. We evaluate a grid of mode seeking hyperparameters (λ, ε) by training 3 independent models for each combination and each dataset. We inspect the results obtained after the adversarial phase, then after the fine-tuning phase, and we compare the results with and without pre-training the State Prediction Network (SPN). λ = 0 means that mode seeking is disabled.

Between the adversarial phase and the fine-tuning phase, the cells in the tables correspond. For example, the first model with λ=0 achieved a minimum modulation loss of 0.198. When SPN pre-training was not chosen, it achieved 0.178 after fine-tuning; when SPN pre-training was performed, it achieved 1.359 after fine-tuning.

For the modulation loss, we show the value obtained during each phase at the best checkpoint.

For the spectral loss, we show the value obtained during each phase at the checkpoint minimizing the spectral loss under the condition that the modulation loss is below 0.9. If this condition cannot be met, the color is magenta.

Slow-LFO Dataset

Modulation loss

Adversarial Phase

ε → λ ↓	0.001	0.01	0.1
0	0.198 0.269 0.209 0.923 0.206 0.297 0.200 0.212 0.443
0.01	0.171 0.253 0.151	1.069 0.181 0.111	0.192 0.054 0.581
0.1	0.169 0.215 0.128	0.222 0.063 0.084	0.117 0.180 0.217
1	0.223 0.155 0.098	0.183 0.187 0.201	0.166 0.242 0.157

Fine-tuning Phase (no SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	0.178 0.358 0.115 1.328 0.086 0.411 0.752 0.197 0.086
0.01	0.125 0.653 0.149	0.277 0.441 0.134	0.238 0.144 0.052
0.1	0.161 0.407 0.403	0.177 0.113 0.113	0.208 0.067 0.383
1	0.743 0.485 0.469	0.270 0.161 0.159	0.108 0.295 0.406

Fine-tuning Phase (with SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	1.359 0.594 0.027 1.302 0.304 0.123 0.190 0.197 0.042
0.01	0.348 0.521 0.365	0.069 0.175 0.089	0.213 0.053 0.359
0.1	0.178 0.197 0.275	0.158 0.040 0.374	0.309 0.193 0.342
1	0.663 0.173 0.160	0.194 0.758 0.155	0.057 0.545 0.215

Spectral (MR-STFT) loss

Adversarial Phase

ε → λ ↓	0.001	0.01	0.1
0	0.892 0.914 0.870 NaN 0.901 1.000 0.943 0.957 0.940
0.01	0.949 0.919 0.928	NaN 0.936 0.933	0.919 0.920 1.045
0.1	0.906 0.924 1.116	0.963 0.897 1.006	1.090 1.090 0.968
1	1.452 1.110 1.069	1.326 1.017 1.106	1.158 1.189 1.005

Fine-tuning Phase (no SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	0.765 0.725 0.644 NaN 0.708 0.724 0.676 0.755 0.643
0.01	0.804 0.680 0.748	0.664 0.708 0.806	0.711 0.770 0.750
0.1	0.770 0.665 0.732	0.782 0.752 0.790	0.766 0.730 0.682
1	0.707 0.757 0.701	0.746 0.775 0.721	0.774 0.766 0.764

Fine-tuning Phase (with SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	NaN 0.719 0.620 NaN 0.717 0.733 0.693 0.737 0.673
0.01	0.803 0.700 0.766	0.640 0.701 0.804	0.687 0.780 0.781
0.1	0.740 0.680 0.732	0.747 0.715 0.793	0.750 0.705 0.680
1	0.715 0.757 0.739	0.726 0.777 0.772	0.806 0.777 0.781

Fast-LFO Dataset

Modulation loss

Adversarial Phase

ε → λ ↓	0.001	0.01	0.1
0	0.025 0.055 0.035 0.041 0.069 0.030 0.034 0.040 0.518
0.01	0.036 0.024 0.030	0.230 0.030 0.043	0.055 0.036 0.025
0.1	0.175 0.200 0.039	0.089 0.036 0.033	0.033 0.224 0.027
1	0.401 0.046 0.141	0.080 0.058 0.365	0.289 0.030 0.033

Fine-tuning Phase (no SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	1.038 0.593 0.388 0.137 0.318 0.304 0.802 0.360 1.045
0.01	1.038 1.032 0.515	0.980 0.820 1.044	0.233 0.309 0.436
0.1	1.029 0.549 0.392	0.626 0.694 0.692	1.007 0.666 1.016
1	0.340 0.958 0.765	0.816 0.655 0.488	0.568 0.686 0.372

Fine-tuning Phase (with SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	0.432 0.185 0.643 0.114 0.561 0.230 0.693 0.489 0.654
0.01	0.704 0.554 0.694	1.036 0.612 1.030	0.568 0.225 0.529
0.1	0.581 0.565 0.917	0.512 0.459 0.670	0.138 0.274 0.225
1	0.358 0.678 0.956	0.740 0.651 0.293	0.609 0.659 0.423

Spectral (MR-STFT) loss

Adversarial Phase

ε → λ ↓	0.001	0.01	0.1
0	0.884 0.880 0.861 0.860 0.904 0.902 0.895 0.910 0.856
0.01	0.864 0.889 0.932	0.861 0.962 0.854	0.921 0.838 0.877
0.1	1.087 0.938 0.993	0.984 0.919 0.960	1.012 1.011 1.036
1	1.044 1.315 0.974	1.627 1.017 1.614	0.952 1.016 1.127

Fine-tuning Phase (no SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	NaN 0.576 0.604 0.479 0.643 0.601 0.658 0.598 NaN
0.01	NaN NaN 0.594	NaN 0.620 NaN	0.573 0.639 0.627
0.1	NaN 0.617 0.636	0.664 0.695 0.641	NaN 0.702 NaN
1	0.558 NaN 0.620	0.761 0.705 0.614	0.762 0.786 0.727

Fine-tuning Phase (with SPN pre-training)

ε → λ ↓	0.001	0.01	0.1
0	0.592 0.534 0.612 0.481 0.646 0.606 0.662 0.595 0.544
0.01	0.660 0.647 0.614	NaN 0.626 NaN	0.577 0.647 0.632
0.1	0.639 0.623 NaN	0.653 0.635 0.636	0.642 0.614 0.622
1	0.572 0.676 NaN	0.711 0.708 0.587	0.768 0.732 0.740