Summary of Reduced Storage Direct Tensor Ring Decomposition For Convolutional Neural Networks Compression, by Mateusz Gabor et al.
Reduced storage direct tensor ring decomposition for convolutional neural networks compression
by Mateusz Gabor, Rafał Zdunek
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 method for compressing convolutional neural networks (CNNs) used in computer vision tasks like image classification. The approach, called reduced storage direct tensor ring decomposition (RSDTR), offers improved flexibility and achieves significant reductions in model size and computational requirements while maintaining accurate classification performance. RSDTR is compared to state-of-the-art compression methods on the CIFAR-10 and ImageNet datasets, showcasing its efficiency in compressing CNNs without sacrificing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to make computer vision models smaller and faster. It uses an approach called reduced storage direct tensor ring decomposition (RSDTR) to shrink convolutional neural networks (CNNs). This helps reduce the amount of memory needed and the time it takes for the model to process images, while keeping its ability to accurately classify pictures intact. The study tests RSDTR on two popular image datasets and shows that it outperforms other methods in compressing CNNs. |
Keywords
» Artificial intelligence » Classification » Image classification
