{"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/colorization-as-a-proxy-task-for-visual","title":"Colorization as a Proxy Task for Visual Understanding","arxiv_id":"1703.04044","date":"2017-03-11","proceeding":"CVPR 2017 7","authors":["Gustav Larsson","Michael Maire","Gregory Shakhnarovich"],"abstract":"We investigate and improve self-supervision as a drop-in replacement for\nImageNet pretraining, focusing on automatic colorization as the proxy task.\nSelf-supervised training has been shown to be more promising for utilizing\nunlabeled data than other, traditional unsupervised learning methods. We build\non this success and evaluate the ability of our self-supervised network in\nseveral contexts. On VOC segmentation and classification tasks, we present\nresults that are state-of-the-art among methods not using ImageNet labels for\npretraining representations.\n  Moreover, we present the first in-depth analysis of self-supervision via\ncolorization, concluding that formulation of the loss, training details and\nnetwork architecture play important roles in its effectiveness. This\ninvestigation is further expanded by revisiting the ImageNet pretraining\nparadigm, asking questions such as: How much training data is needed? How many\nlabels are needed? How much do features change when fine-tuned? We relate these\nquestions back to self-supervision by showing that colorization provides a\nsimilarly powerful supervisory signal as various flavors of ImageNet\npretraining.","url_abs":"http://arxiv.org/abs/1703.04044v3","url_pdf":"http://arxiv.org/pdf/1703.04044v3.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":"colorization-as-a-proxy-task-for-visual","repo_url":"https://github.com/gustavla/self-supervision","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.04044"}},"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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