Summary of When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with Corex, by Bettina Finzel and Patrick Hilme and Johannes Rabold and Ute Schmid
When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX
by Bettina Finzel, Patrick Hilme, Johannes Rabold, Ute Schmid
First submitted to arxiv on: 2 May 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 The proposed method, CoReX, offers a novel approach to explain and evaluate Convolutional Neural Networks (CNNs) by leveraging concept- and relation-based explanations. This is particularly important in complex real-world domains like biology, where the presence of specific concepts and relations between them can significantly impact model decisions. The current pixel relevance-based explanation methods are insufficient, as they fail to convey this type of information. CoReX addresses this limitation by masking (ir-)relevant concepts from the decision-making process and constraining relations in a learned interpretable surrogate model. The approach is tested on several image datasets and CNN architectures, with results showing that CoReX explanations accurately reflect the predictive outcomes of the CNN models. Additionally, human evaluation demonstrates that CoReX is effective in generating combined explanations that help assess the classification quality of CNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoReX is a new way to understand how Convolutional Neural Networks (CNNs) work and why they make certain decisions. Right now, we don’t have a good way to figure out which parts of an image are important for making predictions. This can be a problem when we’re trying to use CNNs for things like medical diagnosis or self-driving cars. The new method uses special explanations that focus on concepts and relationships in the data, rather than just looking at individual pixels. This helps us understand how the model is using this information to make decisions. CoReX was tested with different images and models, and it worked well. People also found it helpful for understanding why CNNs were or weren’t making good predictions. |
Keywords
» Artificial intelligence » Classification » Cnn
