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