## 6 Conclusions and Future Work  
In our exploratory study, we designed, trained, and tested an end-to-end S2I translator with a deep dense generator architecture. We provided detailed information about the model, the heuristics of the approach, and an evaluation of the results from a S2I translation experiment. Additionally, we introduced a solution using informativity classifiers to assess the translator's performance. To the best of our knowledge, this is the first work to address S2I translation without employing supervision or self-supervision for training, and using a dataset characterized by a high degree of audiovisual diversity. Despite the fact that the translator often produced non-informative outputs, our model was capable of generating an average of over 14% interpretable and semantically coherent images. Some of these even exhibited visual structures resembling the input-sound's corresponding images. As discussed throughout the text, the adopted strategy of not using any type of supervision to train the translator ensured that all informative images were necessarily generated through a successful connection between aural and visual modalities. In addition to achieving informativity, the translator was able to produce visually diverse results. We also have found that the translator sometimes produced 'creative' results, picturing original forms, and, less frequently, spontaneously generated multi-informative images. Furthermore, we conducted a performance comparison among five different S2I translator models, varying the dimensionality of the audio embedding space. The results subtly indicated a trade-off between pixel-space convergence and informativity, with better results observed, respectively, for higher and lower feature-space dimensions. We hypothesize that the increased informativity of generated images was due to semantic generalization induced by constraining the flow of information between source and target spaces. However, further studies focusing on quantifying information along the network would be needed to confirm our assumption. Additionally, other explanations for the influence of bottleneck variation on performance must be considered. Apart from the control of information flow, the use of a deeper and denser architecture was decisive for the improvement of the translator's generalization. These solutions, among others which we have presented, allowed us to overcome the problem to a certain extent. We highlight the necessity of further exploring the characteristics of the networks that can interfere in the generalization of GANs applied to cross-modal tasks. We also encourage approaches aiming to optimize  
Figure 14: Multi-informative images translated from sounds of people and fireworks (1 st row on top), and from sounds of people and water (2 nd row). S2I translation was performed from unknown sounds using seven different translator models conditioned on the following audio embedding dimensions: 128 (1 st row (**a,b**)), 512 (1 st row (**c,d**) and 2 nd row (**a–c**)), 1024 (2 nd row (**d**)). the model using perceptual losses jointly with the adversarial loss and the averaged pixel-wise loss. Moreover, finding feasible solutions to address a broader sonic universe is a key step for taking forward the research on S2I translation. Conceptualization, L.A.F. and C.N. ; Data curation, L.A.F. ; Investigation, L.A.F. and C.N. ; Methodology, L.A.F. and C.N. ; Software, L.A.F. ; Supervision, C.N. ; Writing--original draft, L.A.F. ; Writing--review and editing, L.A.F. and C.N. All authors have read and agreed to the published version of the manuscript. The present work was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under PhD grant 200884/2015-8. Also, the work was partly supported by the Spanish State Research Agency (AEI), project PID2019-107579 RB-I00/AEI/10.13039/501100011033.