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We illustrate the method in semantic segmentation, where densely annotated images are aplenty in one domain (synthetic data), but difficult to obtain in another (real images). Current state-of-the-art methods are complex, some requiring adversarial optimization to render the backbone of a neural network invariant to the discrete domain selection variable. Our method does not require any training to perform the domain alignment, just a simple Fourier Transform and its inverse. Despite its simplicity, it achieves state-of-the-art performance in the current benchmarks, when integrated into a relatively standard semantic segmentation model. Our results indicate that even simple procedures can discount nuisance variability in the data that more sophisticated methods struggle to learn away.","url_abs":"https://arxiv.org/abs/2004.05498v1","url_pdf":"https://arxiv.org/pdf/2004.05498v1.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":"fda-fourier-domain-adaptation-for-semantic","repo_url":"https://github.com/YanchaoYang/FDA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"fda-fourier-domain-adaptation-for-semantic","repo_url":"https://github.com/thuml/Transfer-Learning-Library","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fda-fourier-domain-adaptation-for-semantic","repo_url":"https://github.com/albumentations-team/albumentations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"aspp","method_name":"ASPP"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"crf","method_name":"CRF"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deeplabv2","method_name":"DeepLabv2"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sgd","method_name":"SGD"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"spatial-pyramid-pooling","method_name":"Spatial Pyramid Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-cityscapes-to-acdc","task":"Domain Adaptation","dataset":"Cityscapes to ACDC","model":"FDA (DeepLabv2)","rank_in_archive_order":15,"of":16,"metrics":{"mIoU":"45.7"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-panoptic-synthia-to-1","task":"Domain Adaptation","dataset":"Panoptic SYNTHIA-to-Mapillary","model":"FDA","rank_in_archive_order":4,"of":5,"metrics":{"mPQ":"19.1"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-synthia-to-cityscapes","task":"Domain Adaptation","dataset":"SYNTHIA-to-Cityscapes","model":"FDA (VGG-16)","rank_in_archive_order":30,"of":33,"metrics":{"mIoU":"40.5"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-dada-seg","task":"Semantic Segmentation","dataset":"DADA-seg","model":"FDA","rank_in_archive_order":15,"of":28,"metrics":{"mIoU":"24.45"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2004.05498","atlas_url":"https://app.syntology.ai/?focus=2004.05498","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05498"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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