Summary of Till the Layers Collapse: Compressing a Deep Neural Network Through the Lenses Of Batch Normalization Layers, by Zhu Liao et al.
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers
by Zhu Liao, Nour Hezbri, Victor Quétu, Van-Tam Nguyen, Enzo Tartaglione
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 TLC method compresses deep neural networks by reducing the depth of batch normalization layers, decreasing computational requirements and latency. The approach is validated on popular models like Swin-T, MobileNet-V2, and RoBERTa across image classification and NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TLC helps reduce the size of large neural networks, making them more efficient to use. This method is tested on famous models like Swin-T, MobileNet-V2, and RoBERTa for both picture recognition and language processing tasks. |
Keywords
» Artificial intelligence » Batch normalization » Image classification » Nlp
