Summary of Neusemslice: Towards Effective Dnn Model Maintenance Via Neuron-level Semantic Slicing, by Shide Zhou and Tianlin Li and Yihao Huang and Ling Shi and Kailong Wang and Yang Liu and Haoyu Wang
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing
by Shide Zhou, Tianlin Li, Yihao Huang, Ling Shi, Kailong Wang, Yang Liu, Haoyu Wang
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes a novel framework called NeuSemSlice to enable semantic-aware model maintenance tasks for deep neural networks (DNNs). Traditional DNN architectures are monolithic and challenging to maintain, whereas NeuSemSlice addresses this issue by identifying critical neuron-level semantic components. The framework uses a semantic slicing technique to categorize and merge neurons across different categories and layers based on their semantic similarity. This allows for the preservation of model semantics during tasks such as model restructuring, re-adaptation, and incremental development. The authors demonstrate the effectiveness of NeuSemSlice by providing novel strategies for each task and evaluating them against baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuSemSlice is a new way to fix and improve deep learning models. It helps us understand what parts of the model are important and how we can keep those parts working well when we make changes. This is useful because it’s hard to change or update big neural networks without losing their special abilities. NeuSemSlice does this by looking at individual “neurons” in the network and grouping them into categories based on what they do. This lets us keep the important parts of the model working while we make changes. |
Keywords
» Artificial intelligence » Deep learning » Semantics
