{"id":4328,"date":"2023-11-06T22:54:23","date_gmt":"2023-11-06T21:54:23","guid":{"rendered":"https:\/\/www.ceessnoek.info\/?p=4328"},"modified":"2023-11-06T22:54:24","modified_gmt":"2023-11-06T21:54:24","slug":"neurips-2023-learn-to-categorize-or-categorize-to-learn","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/neurips-2023-learn-to-categorize-or-categorize-to-learn\/","title":{"rendered":"NeurIPS 2023: Learn to Categorize or Categorize to Learn?"},"content":{"rendered":"\n<p>The NeurIPS, 2023 cam-ready paper <em>Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery<\/em> by Sarah Rastegar, Hazel Doughty, Cees G M Snoek is <a href=\"https:\/\/arxiv.org\/abs\/2310.19776\">now available.<\/a> In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persists: what exactly delineates a category? In this paper, we conceptualize a category through the lens of optimization, viewing it as an optimal solution to a well-defined problem. Harnessing this unique conceptualization, we propose a novel, efficient and self-supervised method capable of discovering previously unknown categories at test time. A salient feature of our approach is the assignment of minimum length category codes to individual data instances, which encapsulates the implicit category hierarchy prevalent in real-world datasets. This mechanism affords us enhanced control over category granularity, thereby equipping our model to handle fine-grained categories adeptly. Experimental evaluations, bolstered by state-of-the-art benchmark comparisons, testify to the efficacy of our solution in managing unknown categories at test time. Furthermore, we fortify our proposition with a theoretical foundation, providing proof of its optimality.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1308\" height=\"634\" src=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree.png\" alt=\"\" class=\"wp-image-4320\" srcset=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree.png 1308w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree-300x145.png 300w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree-1024x496.png 1024w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree-768x372.png 768w\" sizes=\"auto, (max-width: 1308px) 100vw, 1308px\" \/><\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>The NeurIPS, 2023 cam-ready paper Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery by Sarah Rastegar, Hazel Doughty, Cees G M Snoek is now available. In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-4328","post","type-post","status-publish","format-standard","hentry","category-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NeurIPS 2023: Learn to Categorize or Categorize to Learn? - Cees Snoek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceessnoek.info\/index.php\/neurips-2023-learn-to-categorize-or-categorize-to-learn\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NeurIPS 2023: Learn to Categorize or Categorize to Learn? - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The NeurIPS, 2023 cam-ready paper Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery by Sarah Rastegar, Hazel Doughty, Cees G M Snoek is now available. In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ceessnoek.info\/index.php\/neurips-2023-learn-to-categorize-or-categorize-to-learn\/\" \/>\n<meta property=\"og:site_name\" content=\"Cees Snoek\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-06T21:54:23+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-06T21:54:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2023\/10\/sarah-binary-tree.png\" \/>\n<meta name=\"author\" content=\"Cees\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Cees\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.ceessnoek.info\/index.php\/neurips-2023-learn-to-categorize-or-categorize-to-learn\/\",\"url\":\"https:\/\/www.ceessnoek.info\/index.php\/neurips-2023-learn-to-categorize-or-categorize-to-learn\/\",\"name\":\"NeurIPS 2023: Learn to Categorize or Categorize to Learn? 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