Summary of A Lightweight Measure Of Classification Difficulty From Application Dataset Characteristics, by Bryan Bo Cao et al.
A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
by Bryan Bo Cao, Abhinav Sharma, Lawrence O’Gorman, Michael Coss, Shubham Jain
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach is proposed to efficiently predict the performance of neural network models on datasets with few classes (<10). The conventional method involves repeated training and testing, but this can be time-consuming. The authors introduce an efficient cosine similarity-based classification difficulty measure (S) that can be calculated from dataset metrics and used to compare the relative performance of different models without further training and testing. The proposed method is verified through extensive experiments on various CNN and ViT models and datasets, showing a high correlation with model accuracy. This measure can help practitioners select efficient models, saving time and computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many options for choosing the best neural network model to use in a project. But how do you decide which one will work best? Usually, you would need to train and test each model multiple times, but this can take a lot of time and computing power. A team of researchers has developed a new way to predict how well a model will perform without having to do all that training and testing. They use a special measure called S, which takes into account the characteristics of the dataset and the models themselves. This helps them compare different models quickly and easily, so they can choose the best one for their project. |
Keywords
* Artificial intelligence * Classification * Cnn * Cosine similarity * Neural network * Vit
