USC-DCT: A Collection of Diverse Classification Tasks
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 12, 2023
- Source ID
- 10.3390/data8100153
Entities
People
- Adam M. Jones
- Ao Xu
- Di Wu
- Gozde Sahin
- Kiran Lekkala
- Laurent Itti
- Po-hsuan Huang
- Shuo Ni
- Yuecheng Li
- Yunhao Ge
- Zachary William Murdock
Organizations
- Defense Advanced Research Projects Agency
- United States Army Research Laboratory
- University of Southern California