Tipo di tesi
Tesi di laurea magistrale
Titolo
Explainable emotion recognition via a novel loss function based on informed contrastive learning
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Parole chiave
- Emotion classification
- Explainable artificial intelligence
- Informed machine learning
- Loss function
Data inizio appello
28/04/2023
Riassunto (Inglese)
This thesis work proposes a new loss function, called LogRatio loss and divided into three parts (LogRatioFarthest, LogRatioNearest, LogRatioRandom). This new loss function was used, together with categorical crossentropy, to train an end-to-end architecture that takes in psd data and classifies categorical emotions. For performance comparisons, other architectures were also trained, in one-dimensional, two-dimensional and three-dimensional cases. In the thesis there is a large section where the results that have been obtained in the various experiments are reported.
