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A Comparison of Deep and Shallow Residual Networks for Medical Imaging Classification
DescriptionThe complexity and parameters of mainstream large models are increasing rapidly. For example, the increasingly popular large language models (e.g., ChatGPT) have billions of parameters. While this has led to performance improvements, the performance gains for simple tasks may be unacceptable for the additional cost. We apply residual networks of three different depths and evaluate them extensively on the MedMNIST pneumonia dataset. Experimental results show that smaller models can achieve satisfactory performance at significantly lower costs than larger models.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
TimeWednesday, 15 November 202310am - 5pm MST
LocationDEF Concourse
Artificial Intelligence/Machine Learning
Algorithms
Applications
Architecture and Networks
Cloud Computing
Distributed Computing
Data Analysis, Visualization, and Storage
Performance Measurement, Modeling, and Tools
Programming Frameworks and System Software
TP
XO/EX