3 papers
cs.LG2026
Efficient Neural Network Model Selection for Few-Class Application Datasets
Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman +2
While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can…
cs.CV2025
Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement
Bryan Bo Cao, Lawrence O'Gorman, Michael Coss +1
We propose Few-Class Arena (FCA), as a unified benchmark with focus on testing efficient image classification models for few classes. A wide variety of benchmark datasets with many…
cs.LG2024
A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman +2
Although accuracy and computation benchmarks are widely available to help choose among neural network models, these are usually trained on datasets with many classes, and do not gi…