However, one challenge for these methods is their very high computational cost. In this chapter, we investigate if we can find an optimized CNN model in the classic CNN architecture and if we can do that automatically at a lower cost. Towards this aim, we present a genetic algorithm for ...
不过我一开始都没想到可以编码连接方式,这个思路还是很有启发性的。 Sun, Yanan, et al. "Automatically designing CNN architectures using the genetic algorithm for image classification." IEEE transactions on cybernetics 50.9 (2020): 3840-3854. 这篇文章中对CNN的通道数量和池化层的类型进行了编码。 Sun, ...
Teh, “A fast learning algorithm for deep belief nets,” Neural Comput., vol. 18, no. 7, pp. 1527–1554, 2006.[83] Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy Layer-Wise Training of Deep Networks,” in Advances in Neural Information Processing Systems 19, no...
In this paper, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its "automatic" characteristic that users do not need domain knowledge of CNNs when using ...
Xavier algorithm 初始化所有卷积层、全连层。 1.4 优化器 NAG 可以从实验结果看到,resnet-50结果略好 2. 一些有效的训练方法(提高训练速度) 2.1 Large-batch training。 观察表明:比较大的batch size会造成模型在验证集的精度下降(模型收敛?)。为了解决这的问题可以选择一个一个较大的初始学习率(0.1) ...
Raza, “Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power,” arXiv Prepr. arXiv1807.11682, 2018.[82] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput., vol. 18, no. 7, pp. 1527–1554...
首先由算法(algorithm)生成一系列作为样本的候选框,再通过卷积神经网络进行分类。 常见的算法有 R-CNN、Fast R-CNN、Faster R-CNN 等等。 2) 一阶段(One Stage ) 不需要产生候选框,直接将目标框定位的问题转化为回归(Regression)问题处理(Process)。
and uses the BP algorithm to adjust the parameters, with a classification accuracy of 98.20%. Experimental results show that the technology of combining CNN with transfer learning, data augmentation and SENet improves the performance of livestock image classification, which is an effective application ...
CNN-Based Polarimetric Decomposition Feature Selection for PolSAR Image Classification,程序员大本营,技术文章内容聚合第一站。
本研究考虑在使用计算单元网络的情况下,进行非光滑凸函数分布式优化。我们设定两种规则假设(regularity assumptions)。 1)全局目标函数的利普希茨连续。2)局部单个函数的利普希茨连续。 在局部假设下,我们得到了最优一阶分散式算法(decentralized algorithm)——多步原始对偶(multi-step primal-dual,MSPD)及其对应的最优收...