{"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/the-visual-task-adaptation-benchmark-1","title":"The Visual Task Adaptation Benchmark","arxiv_id":null,"date":"2019-09-25","proceeding":null,"authors":["Xiaohua Zhai","Joan Puigcerver","Alexander Kolesnikov","Pierre Ruyssen","Carlos Riquelme","Mario Lucic","Josip Djolonga","Andre Susano Pinto","Maxim Neumann","Alexey Dosovitskiy","Lucas Beyer","Olivier Bachem","Michael Tschannen","Marcin Michalski","Olivier Bousquet","Sylvain Gelly","Neil Houlsby"],"abstract":"Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets. Yet, the absence of a unified yardstick to evaluate general visual representations hinders progress. Many sub-fields promise representations, but each has different evaluation protocols that are either too constrained (linear classification), limited in scope (ImageNet, CIFAR, Pascal-VOC), or only loosely related to representation quality (generation). We present the Visual Task Adaptation Benchmark (VTAB): a diverse, realistic, and challenging benchmark to evaluate representations. VTAB embodies one principle: good representations adapt to unseen tasks with few examples. We run a large VTAB study of popular algorithms, answering questions like: How effective are ImageNet representation on non-standard datasets? Are generative models competitive? Is self-supervision useful if one already has labels?","url_abs":"https://openreview.net/forum?id=BJena3VtwS","url_pdf":"https://openreview.net/pdf?id=BJena3VtwS","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":[],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[{"slug":"vtab","name":"VTAB","full_name":"Visual Task Adaptation Benchmark"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}