Summary of Characterizing Disparity Between Edge Models and High-accuracy Base Models For Vision Tasks, by Zhenyu Wang et al.
Characterizing Disparity Between Edge Models and High-Accuracy Base Models for Vision Tasks
by Zhenyu Wang, Shahriar Nirjon
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 XDELTA, a novel explainable AI tool that compares high-accuracy base models to computationally efficient but lower-accuracy edge models. The authors propose the DELTA network, a learning-based approach that characterizes model differences by extracting the essence of the base model and ensuring compactness and feature representation capability. A sparsity optimization framework and negative correlation learning approach are used to construct DELTA. The tool is evaluated using over 1.2 million images and 24 models, with results showing it effectively explains differences between base and edge models through geometric and concept-level analysis in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special AI tool that helps us understand why some AI models are good at one task but not as good at another. The authors make this tool by using machine learning to compare the two types of models. They test it with lots of pictures and different AI models, and show that it can really help us understand what’s going on. |
Keywords
» Artificial intelligence » Machine learning » Optimization
