{"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/decision-forests-convolutional-networks-and","title":"Decision Forests, Convolutional Networks and the Models in-Between","arxiv_id":"1603.01250","date":"2016-03-03","proceeding":null,"authors":["Yani Ioannou","Duncan Robertson","Darko Zikic","Peter Kontschieder","Jamie Shotton","Matthew Brown","Antonio Criminisi"],"abstract":"This paper investigates the connections between two state of the art\nclassifiers: decision forests (DFs, including decision jungles) and\nconvolutional neural networks (CNNs). Decision forests are computationally\nefficient thanks to their conditional computation property (computation is\nconfined to only a small region of the tree, the nodes along a single branch).\nCNNs achieve state of the art accuracy, thanks to their representation learning\ncapabilities. We present a systematic analysis of how to fuse conditional\ncomputation with representation learning and achieve a continuum of hybrid\nmodels with different ratios of accuracy vs. efficiency. We call this new\nfamily of hybrid models conditional networks. Conditional networks can be\nthought of as: i) decision trees augmented with data transformation operators,\nor ii) CNNs, with block-diagonal sparse weight matrices, and explicit data\nrouting functions. Experimental validation is performed on the common task of\nimage classification on both the CIFAR and Imagenet datasets. Compared to state\nof the art CNNs, our hybrid models yield the same accuracy with a fraction of\nthe compute cost and much smaller number of parameters.","url_abs":"http://arxiv.org/abs/1603.01250v1","url_pdf":"http://arxiv.org/pdf/1603.01250v1.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":"decision-forests-convolutional-networks-and","repo_url":"https://github.com/PierrickPochelu/word_tree_label","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1603.01250","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}