{"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/one-model-to-learn-them-all","title":"One Model To Learn Them All","arxiv_id":"1706.05137","date":"2017-06-16","proceeding":null,"authors":["Lukasz Kaiser","Aidan N. Gomez","Noam Shazeer","Ashish Vaswani","Niki Parmar","Llion Jones","Jakob Uszkoreit"],"abstract":"Deep learning yields great results across many fields, from speech\nrecognition, image classification, to translation. But for each problem,\ngetting a deep model to work well involves research into the architecture and a\nlong period of tuning. We present a single model that yields good results on a\nnumber of problems spanning multiple domains. In particular, this single model\nis trained concurrently on ImageNet, multiple translation tasks, image\ncaptioning (COCO dataset), a speech recognition corpus, and an English parsing\ntask. Our model architecture incorporates building blocks from multiple\ndomains. It contains convolutional layers, an attention mechanism, and\nsparsely-gated layers. Each of these computational blocks is crucial for a\nsubset of the tasks we train on. Interestingly, even if a block is not crucial\nfor a task, we observe that adding it never hurts performance and in most cases\nimproves it on all tasks. We also show that tasks with less data benefit\nlargely from joint training with other tasks, while performance on large tasks\ndegrades only slightly if at all.","url_abs":"http://arxiv.org/abs/1706.05137v1","url_pdf":"http://arxiv.org/pdf/1706.05137v1.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":"one-model-to-learn-them-all","repo_url":"https://github.com/tensorflow/tensor2tensor","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"model","task_name":"model"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.05137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.05137"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tensorflow/tensor2tensor","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"4116527c2a478716","entry":"problem_hparams_to_features","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"4116527c2a478716"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}