{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-photo-editing-with-introspective","title":"Neural Photo Editing with Introspective Adversarial Networks","arxiv_id":"1609.07093","date":"2016-09-22","proceeding":null,"authors":["Andrew Brock","Theodore Lim","J. M. Ritchie","Nick Weston"],"abstract":"The increasingly photorealistic sample quality of generative image models\nsuggests their feasibility in applications beyond image generation. We present\nthe Neural Photo Editor, an interface that leverages the power of generative\nneural networks to make large, semantically coherent changes to existing\nimages. To tackle the challenge of achieving accurate reconstructions without\nloss of feature quality, we introduce the Introspective Adversarial Network, a\nnovel hybridization of the VAE and GAN. Our model efficiently captures\nlong-range dependencies through use of a computational block based on\nweight-shared dilated convolutions, and improves generalization performance\nwith Orthogonal Regularization, a novel weight regularization method. We\nvalidate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples\nand reconstructions with high visual fidelity.","url_abs":"http://arxiv.org/abs/1609.07093v3","url_pdf":"http://arxiv.org/pdf/1609.07093v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-photo-editing-with-introspective","repo_url":"https://github.com/ajbrock/Neural-Photo-Editor","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"neural-photo-editing-with-introspective","repo_url":"https://github.com/MindSpore-paper-code-3/code2/tree/main/densenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"ian","method_name":"IAN"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"minibatch-discrimination","method_name":"Minibatch Discrimination"},{"method_slug":"multiscale-dilated-convolution-block","method_name":"Multiscale Dilated Convolution Block"},{"method_slug":"orthogonal-regularization","method_name":"Orthogonal Regularization"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"step-decay","method_name":"Step Decay"}],"datasets_introduced":[],"methods_introduced":[{"slug":"multiscale-dilated-convolution-block","name":"Multiscale Dilated Convolution Block","full_name":"Multiscale Dilated Convolution Block"},{"slug":"orthogonal-regularization","name":"Orthogonal Regularization","full_name":"Orthogonal Regularization"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.07093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.07093"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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