Creative Adversarial Networks for Art Generation with Ahmed Elgammal - TWiML Talk #265
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Balancing Aesthetics and Style in GANs
This chapter explores the complexity of tweaking the loss function in Generative Adversarial Networks to achieve a blend of artistic style and ambiguity. It presents a dual-component strategy that balances standard GAN loss with a style ambiguity loss to enhance style diversity while managing aesthetic integrity.
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