Summary of Achieving Data Efficient Neural Networks with Hybrid Concept-based Models, by Tobias A. Opsahl and Vegard Antun
Achieving Data Efficient Neural Networks with Hybrid Concept-based Models
by Tobias A. Opsahl, Vegard Antun
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper introduces two novel hybrid concept-based machine learning models that utilize both class labels and additional information (concepts) in a dataset. To evaluate these models, the authors propose ConceptShapes, an open and flexible dataset with concept labels. The results show that the hybrid models outperform traditional computer vision models and previously proposed concept-based models in terms of accuracy, particularly in sparse data settings. Additionally, the paper presents an algorithm for creating adversarial concept attacks, which can manipulate image perturbations to change a model’s class prediction without affecting its concept predictions. This raises concerns about the interpretability of concept-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a lot of pictures and labels that tell you what’s in each picture, like “dog” or “cat.” But what if you also had information about specific features in those pictures, like “ears” or “whiskers”? This paper shows how using both the label (like “dog”) and these extra details can help train machine learning models more efficiently. The authors create a new way of testing these models called ConceptShapes, which includes labels for these extra details. They find that this approach works better than usual methods in some cases. However, they also show that it’s possible to trick these models into misclassifying pictures by manipulating the extra details without changing what the model thinks is happening in the picture. |
Keywords
» Artificial intelligence » Machine learning
