{"url":"/method/biggan-deep","slug":"biggan-deep","name":"BigGAN-deep","full_name":"BigGAN-deep","full_name_withheld":false,"description_markdown":"**BigGAN-deep** is a deeper version (4x) of [BigGAN](https://paperswithcode.com/method/biggan).  The main difference is a slightly differently designed [residual block](https://paperswithcode.com/method/residual-block). Here the $z$ vector is concatenated with the conditional vector without splitting it into chunks.  It is also based on residual blocks with bottlenecks. BigGAN-deep uses a different strategy than BigGAN aimed at preserving identity throughout the skip connections. In G, where the number of channels needs to be reduced, BigGAN-deep simply retains the first group of channels and drop the rest to produce the required number of channels. In D, where the number of channels should be increased, BigGAN-deep passes the input channels unperturbed, and concatenates them with the remaining channels produced by a 1 × 1 [convolution](https://paperswithcode.com/method/convolution). As far as the\r\nnetwork configuration is concerned, the discriminator is an exact reflection of the generator. \r\n\r\nThere are two blocks at each resolution (BigGAN uses one), and as a result BigGAN-deep is four times\r\ndeeper than BigGAN. Despite their increased depth, the BigGAN-deep models have significantly\r\nfewer parameters mainly due to the bottleneck structure of their residual blocks.","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":"https://github.com/ajbrock/BigGAN-PyTorch/blob/master/BigGANdeep.py","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial 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