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Extending this idea to general visual recognition problems, we\npresent Batch-Instance Normalization (BIN) to explicitly normalize unnecessary\nstyles from images. Considering certain style features play an essential role\nin discriminative tasks, BIN learns to selectively normalize only disturbing\nstyles while preserving useful styles. The proposed normalization module is\neasily incorporated into existing network architectures such as Residual\nNetworks, and surprisingly improves the recognition performance in various\nscenarios. Furthermore, experiments verify that BIN effectively adapts to\ncompletely different tasks like object classification and style transfer, by\ncontrolling the trade-off between preserving and removing style variations. BIN\ncan be implemented with only a few lines of code using popular deep learning\nframeworks.","url_abs":"http://arxiv.org/abs/1805.07925v3","url_pdf":"http://arxiv.org/pdf/1805.07925v3.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":"batch-instance-normalization-for-adaptively","repo_url":"https://github.com/hyeonseob-nam/Batch-Instance-Normalization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"batch-instance-normalization-for-adaptively","repo_url":"https://github.com/hyeonseobnam/batch-instance-normalization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"batch-instance-normalization-for-adaptively","repo_url":"https://github.com/taki0112/Batch_Instance_Normalization-Tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.07925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07925"}},"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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