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There is a growing interest in affective computing research nowadays givenits crucial role in bridging humans with computers. This progress has beenrecently accelerated due to the emergence of bigger data. One recent advance inthis field is the use of adversarial learning to improve model learning throughaugmented samples. However, the use of latent features, which is feasiblethrough adversarial learning, is not largely explored, yet. This technique mayalso improve the performance of affective models, as analogously demonstratedin related fields, such as computer vision. To expand this analysis, in thiswork, we explore the use of latent features through our proposedadversarial-based networks for valence and arousal recognition in the wild.Specifically, our models operate by aggregating several modalities to ourdiscriminator, which is further conditioned to the extracted latent features bythe generator. Our experiments on the recently released SEWA dataset suggestthe progressive improvements of our results. Finally, we show our competitiveresults on the Affective Behavior Analysis in-the-Wild (ABAW) challenge dataset

Adversarial-based neural networks for affect estimations in the wild