预训练支持使用大数据集初始化权重,同时仍然支持网络架构设计的灵活性。 ⑤ One-shot and Zero-shot learning 。One-shot learning通常用于面部识别应用。 一次性学习的一种方法是使用siamese 网络,该网络学习距离函数,这样即使网络仅在一个或几个实例上进行过训练,图像分类也是可能的。另一种非常流行的一次性学习方法...
2.1 Data Augmentations based on basic image manipulations 2.2 Geometric versus photometric transformations 2.3 Data Augmentations based on Deep Learning 3. Design considerations for image Data Augmentation C. Shorten and T. M. Khoshgoftaar, ‘A survey on Image Data Augmentation for Deep Learning’,...
谷歌最早做的自学习增强方法,走的NAS的思路RL+RNN搜索增强空间,还有后来最近发的检测增强也是大同小异,基本就是换汤不换药,问题在于搜索空间太大,复现搜索过于依赖硬件条件(~~普通实验室玩不起~~) 3. Design considerations for image Data Augmentation 3.1 Test-time augmentation 许多都论文指出在检测阶段进行同等...
This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms...
data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of ...
【数据增强】综述:A survey on Image Data Augmentation for Deep Learning,程序员大本营,技术文章内容聚合第一站。
A Survey on Image Data Augmentation for Deep Learning [48pp] @C Geometric transform, color space augment, kernel flters, mixing images, random erasing, feature space augment, adversarial training,...
This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms...
A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019). Article Google Scholar Singer, G. A. C., Fahner, N. A., Barnes, J. G., McCarthy, A. & Hajibabaei, M. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: A case ...
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning.J. Big Data6(1), 1–48 (2019). ArticleGoogle Scholar He, H.et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In2008 IEEE International Joint Conference on Neural Networks ...