Summary of Elrt: Efficient Low-rank Training For Compact Convolutional Neural Networks, by Yang Sui et al.
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
by Yang Sui, Miao Yin, Yu Gong, Jinqi Xiao, Huy Phan, Bo Yuan
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper delves into low-rank training, a novel approach to train convolutional neural networks (CNNs) from scratch, without requiring pre-trained full-rank models. Unlike traditional compression techniques, low-rank training directly optimizes the low-rank structure, offering attractive benefits for practical applications. However, existing solutions face challenges such as accuracy drops and the need to update full-size models. To address these issues, this paper proposes Efficient Low-Rank Training (ELRT), a systematic investigation into low-rank CNN training. ELRT identifies the proper low-rank format and performance-improving strategy to achieve high-accuracy, high-compactness, low-rank CNN models. The paper’s extensive evaluation results demonstrate the effectiveness of ELRT for various CNNs on different datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to train special kinds of computer vision models (CNNs) from scratch without needing huge amounts of data or computational power. This paper explores a new way to do just that – by training these models directly, rather than starting with full-sized ones and then compressing them. While this approach has its advantages, it also comes with some challenges. To overcome these, the researchers propose an efficient method called ELRT (Efficient Low-Rank Training). They tested this method on various CNNs and datasets and found that it leads to high-quality models that are both accurate and compact. |
Keywords
» Artificial intelligence » Cnn
