## 5 Experiments
### S2I Translation Results
#### 5.4.1 Quantitative Evaluation  
By using the set of five informativity classifiers, we were able to make an extensive comparison of the performance levels of different S2I translators. Employing the architecture presented in Section 4.2, the best performance of our translator was obtained with an audio-embedding dimension of 512, resulting in an average informativity of over 14% across all five sound classes. The history of translators' informativity during training is reported in Figure 7a-e. Due to the oscillation of informativity obtained during the model's adversarial training, we evaluated the translator's performance using two averaging metrics: one reports the overall average from epoch 500 to 4000, while the other presents the maximum 50-epochs moving average among the five sound classes (Figure 7f). Additionally, we omitted the informativity data from the two epochs immediately after the discriminator update. Images generated during these epochs often misled the classifiers, leading to erroneous reporting of higher informativity levels despite the presence of visual artifacts in the images. Instead, we relied on the average of the previous five epochs for accurate evaluation. The pixel loss history during the generators' training is also reported using a 50-epochs moving average (Figure 8a). In general, models trained within a broader latent space showed faster convergence and achieved lower pixel losses (Figure 8a,b), although this tendency was not verified in extreme dimensions 128 and 2048. In spite of that, informativity decreased when the embedding dimension was greater than 512 (Figure 7f). Although in a subtle way, this outcome suggests a possible influence of audio embedding dimension variation on translation performance, and the results seem to point out a trade-off between convergence (in the pixel space) and informativity, with better results observed, respectively, for higher and lower feature space dimensionality. The increased flow of information across the network might have led to model overfitting, which is difficult to confirm, since the generators' testing losses are uninformative due to the visual decoupling mentioned in Section 5.2. On the other hand, models trained in a more constrained feature space could present poorer convergence, but they generally produced more informative images, especially for intermediate dimensions. We hypothesize that this occurs due to the semantic generalization induced by constraining the information flow between source and target spaces, as demonstrated in previous studies on supervised learning [66], generative adversarial learning [67; 68], and domain adaptation [69; 70]. In such processes, the reduction of the latent variable dimensionality forces the network to extract essential semantic information. In the case of cross-modal tasks, this helps bridge source and target modalities by exploiting high-level semantics that connect the two. In S2I translation, the concept of an acoustic event is what links aural and visual modalities, and the entirety of the AudioSet [31] structure is based on such concepts. It is also important to emphasize that the proposed S2I translation does not simply prioritize image realism, but also places significant emphasis on informativity. Figure 7: (**a–e**) Translator’s informativity history for each embedding dimension. (**f**) Informativity variation according to embedding dimension. [PERSON] et al. [69] improved GAN training stability for semantic segmentation through bottleneck constraint. We have managed to overcome major instability issues in long-term training using a moving-average discriminator loss, and we haven't identified any correlation between embedding dimension and training stabilization. Therefore, we assume that the improvement of the performance obtained through the bottleneck constraint is not related to GAN stability.