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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | https://arxiv.org/abs/2405.19590 |
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| _version_ | 1866913370034143232 |
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| author | Zhuang, Junbin Din, Guiguang Yan, Yunyi |
| author_facet | Zhuang, Junbin Din, Guiguang Yan, Yunyi |
| contents | Weight play an essential role in deep learning network models. Unlike network structure design, this article proposes the concept of weight augmentation, focusing on weight exploration. The core of Weight Augmentation Strategy (WAS) is to adopt random transformed weight coefficients training and transformed coefficients, named Shadow Weight(SW), for networks that can be used to calculate loss function to affect parameter updates. However, stochastic gradient descent is applied to Plain Weight(PW), which is referred to as the original weight of the network before the random transformation. During training, numerous SW collectively form high-dimensional space, while PW is directly learned from the distribution of SW instead of the data. The weight of the accuracy-oriented mode(AOM) relies on PW, which guarantees the network is highly robust and accurate. The desire-oriented mode(DOM) weight uses SW, which is determined by the network model's unique functions based on WAT's performance desires, such as lower computational complexity, lower sensitivity to particular data, etc. The dual mode be switched at anytime if needed. WAT extends the augmentation technique from data augmentation to weight, and it is easy to understand and implement, but it can improve almost all networks amazingly. Our experimental results show that convolutional neural networks, such as VGG-16, ResNet-18, ResNet-34, GoogleNet, MobilementV2, and Efficientment-Lite, can benefit much at little or no cost. The accuracy of models is on the CIFAR100 and CIFAR10 datasets, which can be evaluated to increase by 7.32\% and 9.28\%, respectively, with the highest values being 13.42\% and 18.93\%, respectively. In addition, DOM can reduce floating point operations (FLOPs) by up to 36.33\%. The code is available at https://github.com/zlearh/Weight-Augmentation-Technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weights Augmentation: it has never ever ever ever let her model down Zhuang, Junbin Din, Guiguang Yan, Yunyi Machine Learning Weight play an essential role in deep learning network models. Unlike network structure design, this article proposes the concept of weight augmentation, focusing on weight exploration. The core of Weight Augmentation Strategy (WAS) is to adopt random transformed weight coefficients training and transformed coefficients, named Shadow Weight(SW), for networks that can be used to calculate loss function to affect parameter updates. However, stochastic gradient descent is applied to Plain Weight(PW), which is referred to as the original weight of the network before the random transformation. During training, numerous SW collectively form high-dimensional space, while PW is directly learned from the distribution of SW instead of the data. The weight of the accuracy-oriented mode(AOM) relies on PW, which guarantees the network is highly robust and accurate. The desire-oriented mode(DOM) weight uses SW, which is determined by the network model's unique functions based on WAT's performance desires, such as lower computational complexity, lower sensitivity to particular data, etc. The dual mode be switched at anytime if needed. WAT extends the augmentation technique from data augmentation to weight, and it is easy to understand and implement, but it can improve almost all networks amazingly. Our experimental results show that convolutional neural networks, such as VGG-16, ResNet-18, ResNet-34, GoogleNet, MobilementV2, and Efficientment-Lite, can benefit much at little or no cost. The accuracy of models is on the CIFAR100 and CIFAR10 datasets, which can be evaluated to increase by 7.32\% and 9.28\%, respectively, with the highest values being 13.42\% and 18.93\%, respectively. In addition, DOM can reduce floating point operations (FLOPs) by up to 36.33\%. The code is available at https://github.com/zlearh/Weight-Augmentation-Technology. |
| title | Weights Augmentation: it has never ever ever ever let her model down |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.19590 |