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The baseline and incremental changes are:\r\n\r\n- Using [SAGAN](https://paperswithcode.com/method/sagan) as a baseline with spectral norm. for G and D, and using [TTUR](https://paperswithcode.com/method/ttur).\r\n- Using a Hinge Loss [GAN](https://paperswithcode.com/method/gan) objective\r\n- Using class-[conditional batch normalization](https://paperswithcode.com/method/conditional-batch-normalization) to provide class information to G (but with linear projection not MLP.\r\n- Using a [projection discriminator](https://paperswithcode.com/method/projection-discriminator) for D to provide class information to D.\r\n- Evaluating with EWMA of G's weights, similar to ProGANs.\r\n\r\nThe innovations are:\r\n\r\n- Increasing batch sizes, which has a big effect on the Inception Score of the model.\r\n- Increasing the width in each layer leads to a further Inception Score improvement.\r\n- Adding skip connections from the latent variable $z$ to further layers helps performance.\r\n- A new variant of [Orthogonal Regularization](https://paperswithcode.com/method/orthogonal-regularization).","description_state":"present","introduced_year":null,"introduced_by":{"title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","paper":"/paper/large-scale-gan-training-for-high-fidelity","first_author":"Andrew Brock","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/large-scale-gan-training-for-high-fidelity"},"source":{"url":"http://arxiv.org/abs/1809.11096v2","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial Networks","url":"/methods/category/generative-adversarial-networks","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative 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