Summary of Training Over a Distribution Of Hyperparameters For Enhanced Performance and Adaptability on Imbalanced Classification, by Kelsey Lieberman et al.
Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
by Kelsey Lieberman, Swarna Kamlam Ravindran, Shuai Yuan, Carlo Tomasi
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the problem of training reliable classifiers when dealing with severe class imbalance in binary classification tasks. While previous techniques have addressed this issue by modifying loss functions or optimization methods, the authors observe that different hyperparameter values perform better at different recall values. To exploit this observation, they propose Loss Conditional Training (LCT), which trains a model over a distribution of hyperparameter values instead of a single value. The results show that training with LCT not only approximates the performance of multiple models but also improves overall performance on both CIFAR and real-world medical imaging applications, such as melanoma and diabetic retinopathy detection. Moreover, training with LCT is more efficient since some hyperparameter tuning can be conducted after training without needing to retrain from scratch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in computer science called class imbalance. Imagine you’re trying to teach a computer to recognize pictures of cats and dogs, but 99% of the pictures are of dogs! This makes it hard for the computer to learn about the rare case where it sees a cat. The researchers found that different settings can help the computer do better in these situations, so they developed a new way to train computers called Loss Conditional Training (LCT). They tested this method on some tricky tasks like recognizing skin cancer from pictures and detecting eye problems. Surprisingly, LCT worked really well! It not only made the computer better at doing these tasks but also saved time by allowing it to fine-tune its performance without having to start all over again. |
Keywords
» Artificial intelligence » Classification » Hyperparameter » Optimization » Recall
