Summary of Benchmarking Generative Ai Models For Deep Learning Test Input Generation, by Maryam et al.
Benchmarking Generative AI Models for Deep Learning Test Input Generation
by Maryam, Matteo Biagiola, Andrea Stocco, Vincenzo Riccio
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
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 paper introduces Test Input Generators (TIGs), essential tools to evaluate the performance of Deep Learning (DL) image classifiers on unseen inputs. By leveraging recent advancements in Generative AI (GenAI) models, TIGs can create synthetic images that mimic real-world scenarios, allowing for more comprehensive assessments of DL model capabilities. The increased complexity and resource demands associated with GenAI models are discussed, highlighting the need for efficient and effective TIGs to ensure reliable predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating fake pictures to test how well AI image recognition systems work on new, unseen images. Right now, there’s a big problem in artificial intelligence (AI) because we don’t have good ways to test if these systems will work correctly when shown images they’ve never seen before. This paper proposes a solution using special computer programs that can create fake pictures that are similar to real ones. These fake pictures can be used to test how well the AI system works, which is important for making sure it makes correct predictions in real-life situations. |
Keywords
» Artificial intelligence » Deep learning
