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towardsdatascience.com/everything-you-need-to-know-about-saving-weights-in-pytorch-572651f3f8detowardsdatascience.com/everything-you-need-to-know-about-saving-weights-in-pytorch-572651f3f8de Everything You Need To Know About Saving Weights In PyTorch What do we Deep Learning practitioners do once we are done with training our models ? We Chill ! towardsdatascience.com towards data science의 위 글을 ..

https://arxiv.org/abs/1912.01865 StarGAN v2: Diverse Image Synthesis for Multiple Domains A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the iss arxiv.org StarGAN v2 논문을 정리한 글입니다! 오역한 부분이나 자연스러운 표..

https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431 Explained: A Style-Based Generator Architecture for GANs - Generating and Tuning Realistic… NVIDIA’s novel architecture for Generative Adversarial Networks towardsdatascience.com towards data science의 위 글을 번역한 글입니다! 오역한 부분이나 자연스러운 표현을 위해 의역한 부분이 있을 수 있습니다. 별(*)로 시작하는..

https://arxiv.org/abs/1703.10593 Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be a arxiv.org Cycle GAN 논문을 정리 및..