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Exploring the Evolution of GANs
through Quality Diversity
UNIVERSITÀ DEGLI STUDI DI TRIESTE
Dipartimento di Ingegneria e Architettura
Laurea Triennale in Ingegneria Elettronica e Informatica
Laureando
Stefano Chen
Relatore
Prof. Andrea De Lorenzo
Costa V., Lourenço N., Correia J., & Machado P. (2020, June). Exploring the evolution of GANs through quality diversity.
In Proceedings of the 2020 Genetic and Evolutionary Computation Conference
Generative Adversarial Network
❏ Deep Learning
Model
- Image Generation
❏ Due Reti Neurali:
- Generatore
- Discriminatore
❏ Fase di Training:
- Interazione
competitiva
Problemi
❏ Vanishing Gradient
- Discriminatore
Ottimale
- Stagnazione
❏ Model Collapse
- Generatore
Debole
- Poca Diversità
Modello Proposto
NeuroEvoluzione e
Coevoluzione
COEGAN
Quality Diversity
Algorithm with
neighborhood
NSLC
Funzionamento
❏ Inizializzazione
- 2 sottopopolazioni
- Genoma - Gene
❏ Valutazione
- All vs All
- FID (Fréchet Inception
Distance)
❏ Crossover
- Vicinato (n individui)
- Innovation e competition score
❏ Mutazione
❏ Sostituzione
Inizializzazione
Valutazione
Crossover
Mutazione
Sostituzione
Esperimenti
❏ MNIST
- COEGAN
- COEGAN+NSLC
- COEGAN+NSGC
❏ CelebA
- COEGAN+NSGC
- DCGAN
MNIST dataset
Migliori Generatori N. Training Samples
MNIST dataset
b
CelebA dataset
Immagini Generate Figura c
DCGAN training epoch = 1000 batches
COEGAN+NSGC training epoch = 1 generation (5x200 batches)
Conclusioni
❏ Pros:
- Immagini migliori
- Meno training samples
- Resistente al vanishing gradient
- Resistente al model collapse
❏ Cons:
- Maggiore costo computazionale
Grazie per l’attenzione

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Exploring the evolution of GANs through quality diversity