{"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/deep-ten-texture-encoding-network","title":"Deep TEN: Texture Encoding Network","arxiv_id":"1612.02844","date":"2016-12-08","proceeding":"CVPR 2017 7","authors":["Hang Zhang","Jia Xue","Kristin Dana"],"abstract":"We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding\nLayer integrated on top of convolutional layers, which ports the entire\ndictionary learning and encoding pipeline into a single model. Current methods\nbuild from distinct components, using standard encoders with separate\noff-the-shelf features such as SIFT descriptors or pre-trained CNN features for\nmaterial recognition. Our new approach provides an end-to-end learning\nframework, where the inherent visual vocabularies are learned directly from the\nloss function. The features, dictionaries and the encoding representation for\nthe classifier are all learned simultaneously. The representation is orderless\nand therefore is particularly useful for material and texture recognition. The\nEncoding Layer generalizes robust residual encoders such as VLAD and Fisher\nVectors, and has the property of discarding domain specific information which\nmakes the learned convolutional features easier to transfer. Additionally,\njoint training using multiple datasets of varied sizes and class labels is\nsupported resulting in increased recognition performance. The experimental\nresults show superior performance as compared to state-of-the-art methods using\ngold-standard databases such as MINC-2500, Flickr Material Database,\nKTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The\nsource code for the complete system are publicly available.","url_abs":"http://arxiv.org/abs/1612.02844v1","url_pdf":"http://arxiv.org/pdf/1612.02844v1.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":"deep-ten-texture-encoding-network","repo_url":"https://github.com/zhanghang1989/Deep-Encoding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/CWanli/myencoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/Praveen94/pytorch-encoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/RyanHTR/PyTorch-Encoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/etmwb/cvsegmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/kmaninis/pytorch-encoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/vijayiitkgp/textue_ensemble_framework","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/xllau/PyTorch-Encoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/zhanghang1989/PyTorch-Encoding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/zhanghang1989/Torch-Encoding-Layer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok"}},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/zhusiling/EncNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-ten-texture-encoding-network","repo_url":"https://github.com/zhusiling/Pytorch-Encoding-boundary","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"material-recognition","task_name":"Material Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.02844","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.02844"}},"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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