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In\nthis paper, we revisit the problem of purely unsupervised image segmentation\nand propose a novel deep architecture for this problem. We borrow recent ideas\nfrom supervised semantic segmentation methods, in particular by concatenating\ntwo fully convolutional networks together into an autoencoder--one for encoding\nand one for decoding. The encoding layer produces a k-way pixelwise prediction,\nand both the reconstruction error of the autoencoder as well as the normalized\ncut produced by the encoder are jointly minimized during training. When\ncombined with suitable postprocessing involving conditional random field\nsmoothing and hierarchical segmentation, our resulting algorithm achieves\nimpressive results on the benchmark Berkeley Segmentation Data Set,\noutperforming a number of competing methods.","url_abs":"http://arxiv.org/abs/1711.08506v1","url_pdf":"http://arxiv.org/pdf/1711.08506v1.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":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/Andrew-booler/W-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/Joesher15/deep_learning_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/craig-m-k/W-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/gerardrbentley/Pytorch-U-Net-AutoEncoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/kimtae55/DeepFDR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/raun1/Complementary_Segmentation_Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/raun1/Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/raun1/MICCAI2018---Complementary_Segmentation_Network-Raw-Code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/raun1/MICCAI2018---Complementary_Segmentation_Network-Raw-Code-Available-Under-Construction-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/taoroalin/WNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"w-net-a-deep-model-for-fully-unsupervised","repo_url":"https://github.com/wau/Unsupervised-SIS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-image-segmentation","task_name":"Unsupervised Image Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.08506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08506"}},"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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