USC-DCT: A Collection of Diverse Classification Tasks

Adam M. Jones, Gozde Sahin, Zachary W. Murdock, Yunhao Ge, Ao Xu, Yuecheng Li, Di Wu, Shuo Ni, Po-Hsuan Huang, Kiran Lekkala and Laurent Itti ()
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Adam M. Jones: Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90007, USA
Gozde Sahin: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Zachary W. Murdock: Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90007, USA
Yunhao Ge: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Ao Xu: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Yuecheng Li: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Di Wu: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Shuo Ni: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Po-Hsuan Huang: Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90007, USA
Kiran Lekkala: Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA
Laurent Itti: Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90007, USA

Data, 2023, vol. 8, issue 10, 1-22

Abstract: Machine learning is a crucial tool for both academic and real-world applications. Classification problems are often used as the preferred showcase in this space, which has led to a wide variety of datasets being collected and utilized for a myriad of applications. Unfortunately, there is very little standardization in how these datasets are collected, processed, and disseminated. As new learning paradigms like lifelong or meta-learning become more popular, the demand for merging tasks for at-scale evaluation of algorithms has also increased. This paper provides a methodology for processing and cleaning datasets that can be applied to existing or new classification tasks as well as implements these practices in a collection of diverse classification tasks called USC-DCT. Constructed using 107 classification tasks collected from the internet, this collection provides a transparent and standardized pipeline that can be useful for many different applications and frameworks. While there are currently 107 tasks, USC-DCT is designed to enable future growth. Additional discussion provides explanations of applications in machine learning paradigms such as transfer, lifelong, or meta-learning, how revisions to the collection will be handled, and further tips for curating and using classification tasks at this scale.

Keywords: machine learning; data sharing; classification; computer vision; visual classification; dataset collection; dataset organization; data cleaning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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