Summary of Training Morphological Neural Networks with Gradient Descent: Some Theoretical Insights, by Samy Blusseau (cmm)
Training morphological neural networks with gradient descent: some theoretical insights
by Samy Blusseau
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 morphological neural network architectures have the potential to revolutionize mathematical morphology and image processing pipelines. However, training these networks becomes increasingly challenging as the number of morphological layers increases, even with popular machine learning frameworks that rely on gradient descent optimization algorithms. To address this limitation, the authors explore differentiation-based approaches and back-propagation techniques applied to morphological networks, leveraging the concept of Bouligand derivatives and non-smooth optimization. Theoretical guidelines are provided for initialization and learning rates, offering insights into the potential and limitations of these innovative architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Morphological neural networks can be used to improve image processing and mathematical morphology. But training these networks gets harder when you have many layers. To make this work better, researchers investigated new ways to optimize morphological networks using Bouligand derivatives. They found that the key is in how you initialize and adjust the learning rates. |
Keywords
* Artificial intelligence * Gradient descent * Machine learning * Neural network * Optimization
