Summary of Improving Dnn Modularization Via Activation-driven Training, by Tuan Ngo et al.
Improving DNN Modularization via Activation-Driven Training
by Tuan Ngo, Abid Hassan, Saad Shafiq, Nenad Medvidovic
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed paper introduces MODA, an activation-driven modular training approach that aims to improve the reusability of Deep Neural Networks (DNNs). The current state-of-the-art techniques for decomposing DNN models during and after training suffer from significant weight overlaps, accuracy losses, and added complexity. MODA addresses these shortcomings by regulating the activation outputs of layers based on three modular objectives: intra-class affinity, inter-class dispersion, and compactness. The proposed approach is evaluated using three well-known DNN models and three datasets with varying sizes, demonstrating several advantages over existing state-of-the-art methods, including reduced training time, fewer weights, less weight overlap, preserved original accuracy, and improved module replacement scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make Deep Neural Networks more reusable. Right now, it’s hard to change these networks when their requirements change. To solve this problem, the researchers propose an “activation-driven modular training approach” that helps the network learn how to be broken down into smaller parts (modules) while still being accurate. This approach is tested on three different types of neural networks and three datasets with varying sizes, showing that it can reduce the time needed to train the network, use fewer weights, have less overlap between modules, and preserve the original accuracy of the network. |
