Summary of Detecting and Approximating Redundant Computational Blocks in Neural Networks, by Irene Cannistraci et al.
Detecting and Approximating Redundant Computational Blocks in Neural Networks
by Irene Cannistraci, Emanuele Rodolà, Bastian Rieck
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper investigates the emergence of internal similarities across different layers in diverse neural architectures, showing that similarity patterns emerge independently of the dataset used. The authors introduce Block Redundancy, a simple metric to detect redundant blocks, and propose Redundant Blocks Approximation (RBA), a framework that identifies and approximates one or more redundant computational blocks using simpler transformations. RBA reduces model parameters and time complexity while maintaining good performance, validated on classification tasks in the vision domain using various pretrained foundational models and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how deep neural networks learn similar things inside themselves, both across different models and within their own layers. This helps us understand how to design better architectures for these networks. The authors find that similar patterns emerge no matter what dataset is used. They also introduce a simple way to measure if blocks in the network are redundant, called Block Redundancy. From this, they develop a method called Redundant Blocks Approximation (RBA) that can replace redundant blocks with simpler transformations. RBA makes models smaller and faster while still working well. |
Keywords
* Artificial intelligence * Classification
