感受野、下采样相关

最新推荐文章于 2024-09-12 15:51:30 发布

wuxtwu

最新推荐文章于 2024-09-12 15:51:30 发布

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文章标签：感受野、下采样、空洞卷积

本文链接：https://blog.youkuaiyun.com/xnmc2014/article/details/85332960

本文探讨了深度卷积神经网络中的感受野概念，解释了其对网络工作原理的影响。感受野大小决定了网络单元对输入的依赖区域。通过增加网络层数、下采样或使用空洞卷积可以增大感受野。下采样虽然能减小表示尺寸，但也可能导致信息损失。文章提到了去除池化操作的趋势，特别是在全卷积网络和生成模型中，以及PyTorch中尺寸计算的方法。

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关于感受野：

“One of the basic concepts in deep CNNs is the receptive field, or field of view, of a unit in a certain layer in the network. Unlike in fully connected networks, where the value of each unit depends on the entire input to the network, a unit in convolutional networks only depends on a region of the input. This region in the input is the receptive field for that unit
The concept of receptive field is important for understanding and diagnosing how deep CNNs work. Since anywhere in an input image outside the receptive field of a unit does not affect the value of that unit, it is necessary to carefully control the receptive field, to ensure that it covers the entire relevant image region. In many tasks, especially dense prediction tasks like semantic image segmentation, stereo and optical flow estimation, where we make a prediction for each single pixel in the input image, it is critical for each output pixel to have a big rec