3.2 How the ERF evolves during training 4 Reduce the Gaussian Damage 5 Discussion 参考资料 Understanding the Effective Receptive Field inDeep Convolutional Neural Networks (NIPS 2016) 读这篇论文是因为在前一篇论文Large Kernel里面作者提到了这个概念,在实际的网络中感受野的大小达不到我们计算的理论感受野的...
有效感受野--Understanding the Effective Receptive Field in Deep Convolutional Neural Networks,程序员大本营,技术文章内容聚合第一站。
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks NIPS 2016 本文主要分析了 CNN 网络中的Receptive Field,发现实际有效的感受野 和理论上的感受野 差距比较大,实际有效的感受野是一个高斯分布。 We introduce the notion of an erf、erfc公式及其函数值表查询 1.误差函数erf(也称为...
This indicates that o(t) decays from the center of the receptive field squared exponentially according to the Gaussian distribution. The rate of decay is related to the variance of this Gaussian. If we take one standard deviation as theeffective receptive field(ERF) size which is roughly t...
Intriguingly, as the networks learns, the ERF gets bigger, andat the end of training is significantly larger than the initial ERF. In Fig. 3 we show the effectivereceptive field on the 32×32 image space at the beginning of training (with randomly initializedweights) and at the end of ...
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks 相关参考 https://zhuanlan.zhihu.com/p/86599013 ERF比理论感受野小 近似成高斯分布 subsampling、diation conv 可增大ERF 训练过程中ERF不断增大
In this paper, we present an effective receptive field (eRF)-dependent region proposal network (eRPN) for proposal generation, which enhances the anchor-based representation via eRFs. Specifically, we define an eRF for each sliding window on the feature map and only encode objects within the ...
Subsampling & dilated convolution increases receptive field: 下面的图像显示了subsampling和空洞卷积的效果。参考baseline是15个dense convolution层组成的Convnet。 3.2 How the ERF evolves during training我们分析了在分类CNN和语义分割CNN最上层单元的ERF是如何变化的,对于这两个任务,我们采用了ResNet架构,它广泛地使...
Previously, we devised strategies for building compact dense prediction networks guided by the effective receptive field (ERF) characteristics of the network (DDCNet). The DDCNet design was intentionally simple and compact allowing it to be used as a building block for designing more complex yet ...
感受野感受野(receptivefield, RF),卷积神经网络每一层输出的特征图(feature map)上的特征点在原始图像上映射的区域大小,即特征点能“看”到的...(effectivereceptivefield, ERF),在卷积计算时,实际有效的感受野区域。在F0特征层中,特征点6可以描述其他所有特征点的部分信息,即图中交叠部分,特征点6代表的信息更 ...