Summary of Deepknowledge: Generalisation-driven Deep Learning Testing, by Sondess Missaoui et al.
DeepKnowledge: Generalisation-Driven Deep Learning Testing
by Sondess Missaoui, Simos Gerasimou, Nikolaos Matragkas
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 Deep neural networks (DNNs) have achieved remarkable success, but they are highly susceptible to small shifts in data distribution. This fragility demands effective testing techniques that can assess their dependability. Despite recent advances in DNN testing, there is a lack of systematic approaches that evaluate the DNN’s capability to generalize and operate comparably beyond data in their training distribution. To address this gap, we propose DeepKnowledge, a systematic testing methodology founded on the theory of knowledge generalization, aiming to enhance DNN robustness and reduce the residual risk of “black box” models. DeepKnowledge posits that core computational units, termed Transfer Knowledge neurons, can generalize under domain shift. It provides an objective confidence measurement on testing activities given data distribution shifts and uses this information to instrument a generalization-informed test adequacy criterion to check the transfer knowledge capacity of a test set. Our empirical evaluation demonstrates the usefulness and effectiveness of DeepKnowledge, supporting the engineering of more dependable DNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are super smart but really bad at handling small changes in data. They need special testing to make sure they’re reliable. Right now, there aren’t many ways to test them well, so we came up with a new method called DeepKnowledge. It’s based on an idea about how computers can learn from each other. We used this idea to create tests that check if the computer is good at learning from one type of data and then using it in another situation. We tested our method on several different computer programs and types of data, and it worked really well! We even got better results than some other popular testing methods. |
Keywords
* Artificial intelligence * Generalization
