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- Title
USC-DCT: A Collection of Diverse Classification Tasks.
- Authors
Jones, Adam M.; Sahin, Gozde; Murdock, Zachary W.; Ge, Yunhao; Xu, Ao; Li, Yuecheng; Wu, Di; Ni, Shuo; Huang, Po-Hsuan; Lekkala, Kiran; Itti, Laurent
- 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. Dataset: https://github.com/iLab-USC/USC-DCT Dataset License: CC-BY-NC
- Subjects
UNITED States Code; MACHINE learning; TASK analysis; CLASSIFICATION; COLLECTIONS; COMPUTER vision
- Publication
Data (2306-5729), 2023, Vol 8, Issue 10, p153
- ISSN
2306-5729
- Publication type
Article
- DOI
10.3390/data8100153