{"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/inverse-compositional-spatial-transformer","title":"Inverse Compositional Spatial Transformer Networks","arxiv_id":"1612.03897","date":"2016-12-12","proceeding":"CVPR 2017 7","authors":["Chen-Hsuan Lin","Simon Lucey"],"abstract":"In this paper, we establish a theoretical connection between the classical\nLucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer\nNetworks (STNs). STNs are of interest to the vision and learning communities\ndue to their natural ability to combine alignment and classification within the\nsame theoretical framework. Inspired by the Inverse Compositional (IC) variant\nof the LK algorithm, we present Inverse Compositional Spatial Transformer\nNetworks (IC-STNs). We demonstrate that IC-STNs can achieve better performance\nthan conventional STNs with less model capacity; in particular, we show\nsuperior performance in pure image alignment tasks as well as joint\nalignment/classification problems on real-world problems.","url_abs":"http://arxiv.org/abs/1612.03897v1","url_pdf":"http://arxiv.org/pdf/1612.03897v1.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":"inverse-compositional-spatial-transformer","repo_url":"https://github.com/chenhsuanlin/inverse-compositional-STN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.03897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.03897"}},"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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