深入理解空洞卷积
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2020-12-07 17:09
导读
空洞卷积在图像分割需要增加感受野同时保持特征图的尺寸的需求中诞生,本文详细介绍了空洞卷积的诞生、原理、计算过程以及存在的两个潜在的问题,帮助大家将空洞卷积这一算法“消化吸收”。
一、空洞卷积的提出
![](https://filescdn.proginn.com/63da6ed6e0d6f41085056e6c184bfd52/cff29f3411ae227d313fe0dce8374373.webp)
二、空洞卷积的原理
![](https://filescdn.proginn.com/ec3a9294dfc98a2d7db591a8a6d4f2a0/2d96f3e545a92cd937de7b5cb1624f9d.webp)
![](https://filescdn.proginn.com/470cbd9c0d15078817ae82fce5e9b441/c9f8baa2f5b2a9f440051939f74e8de4.webp)
![](https://filescdn.proginn.com/ec3a9294dfc98a2d7db591a8a6d4f2a0/2d96f3e545a92cd937de7b5cb1624f9d.webp)
a是普通的卷积过程(dilation rate = 1),卷积后的感受野为3 b是dilation rate = 2的空洞卷积,卷积后的感受野为5 c是dilation rate = 3的空洞卷积,卷积后的感受野为8
![](https://filescdn.proginn.com/59e49ed54d31f196b756a93b83c6f18f/296b3433bd8015108ca82835a41440d2.webp)
![](https://filescdn.proginn.com/65cb853a7322617f5fdfa935aaf2e9cc/4fe8da2b512d2905e887fa2d2e261b7e.webp)
![](https://filescdn.proginn.com/93c10a3890365168e88227fe433da3b4/fa9a6f4a96ddb6c27cbab6e42b33e672.webp)
![](https://filescdn.proginn.com/b0a022253e0521deecc2f27db3517da2/4e1038d41996996c378257edbfb32b09.webp)
![](https://filescdn.proginn.com/caba91b63ae832d12811bdf03fd18002/c6a8abdeb461fb973839d4c0e4e62e28.webp)
![](https://filescdn.proginn.com/92a6216c4f5d61713729f023b034a950/5116e73bded7fc9633de7740dbbfae8d.webp)
dense prediction problems such as semantic segmentation ... to increase the performance of dense prediction architectures by aggregating multi-scale contextual information(来自[1])
三、感受野的计算
![](https://filescdn.proginn.com/470cbd9c0d15078817ae82fce5e9b441/c9f8baa2f5b2a9f440051939f74e8de4.webp)
![](https://filescdn.proginn.com/ec3a9294dfc98a2d7db591a8a6d4f2a0/2d96f3e545a92cd937de7b5cb1624f9d.webp)
![](https://filescdn.proginn.com/7e490cfc59d78d829a8271341bf86d5f/7a7c8fa8691931ca49ce6aa2394ac7c5.webp)
![](https://filescdn.proginn.com/e5ed08cdc772df40d98420eaa06a54db/4f2f990be5578dca6c89e65231f11a3d.webp)
![](https://filescdn.proginn.com/339cc57671f3acb6c74398d72157f819/3e17b2cb0d1dce444eaa83f645c37905.webp)
![](https://filescdn.proginn.com/e8562f00c21ceb0a505af81edcf78254/b92fb0d2447d5b7edeb9128dc11db3f9.webp)
![](https://filescdn.proginn.com/cb4674e5dd2a7961b17c881035804511/1244ae68eb95945798f2d9e045a2d1f1.webp)
![](https://filescdn.proginn.com/ec3a9294dfc98a2d7db591a8a6d4f2a0/2d96f3e545a92cd937de7b5cb1624f9d.webp)
![](https://filescdn.proginn.com/effbaf0286a937ce7d7ddd9c432ce043/d450e4532a4858cc67449983e5eeddd9.webp)
![](https://filescdn.proginn.com/880141e0d6240894f879c1b035b94f7b/7f507ea8d286953163403c93957873a2.webp)
当前层的感受野计算公式如下,其中,
![](https://filescdn.proginn.com/8c36f9ef5bfe86572d8b69a172ba1759/d7aea5eac42572b07336f23a70c34836.webp)
![](https://filescdn.proginn.com/c8d0e28d21ee1a2451a26514825d85cf/9bddd98844669abec996c569da2c2814.webp)
![](https://filescdn.proginn.com/cb4674e5dd2a7961b17c881035804511/1244ae68eb95945798f2d9e045a2d1f1.webp)
![](https://filescdn.proginn.com/3989bafb478d1f83c6c9caf145089be6/c58c06517f3628fb583158095961c673.webp)
![](https://filescdn.proginn.com/c7b9122f4ebb09fd6727ee7564f331e7/e89ddd8cbd4ef0e19774679b85955a55.webp)
![](https://filescdn.proginn.com/baa0a3efe43808da47f9c87f2f0aefce/b062ab4113e553bac01684c8901091cf.webp)
![](https://filescdn.proginn.com/014edccb2ee8245aa0d5913d001fae88/9ba50e72b6724c8e8cb6c797590e888c.webp)
![](https://filescdn.proginn.com/52b83050a330fc32d62a7cb85476705d/486c473762c1f189ab83626b2206ddc9.webp)
![](https://filescdn.proginn.com/c4a7c8475233b8255628c7fc6d8ed1de/25ce2f796e09ed27f2c87a9cb56905dc.webp)
![](https://filescdn.proginn.com/38e85104cb2e6a0e1f0cf5636b363ccf/6412263d9566f9e9b59222a8a245f312.webp)
![](https://filescdn.proginn.com/c60c9effa0126e23141cebe311b4cf0f/5bd0f0f77ec8947949e0c3c5539623d6.webp)
![](https://filescdn.proginn.com/56d181f75d268765347492042e26101d/dbe718348b7e6c13107e7501679dddae.webp)
![](https://filescdn.proginn.com/118d48a416faf739374bfce9b2661dd9/45b0d3fe7ff56c66c19a14484aa9f2df.webp)
四、潜在的问题及解决方法
![](https://filescdn.proginn.com/9193ee916547b3f2c4bb1592ed4b95f1/3e7eaf264320e1761f76e9e6dfce7e47.webp)
Panqu Wang,Pengfei Chen, et al**.Understanding Convolution for Semantic Segmentation.//**WACV 2018 Fisher Yu, et al. Dilated Residual Networks. //CVPR 2017 Zhengyang Wang,et al.**Smoothed Dilated Convolutions for Improved Dense Prediction.//**KDD 2018. Liang-Chieh Chen,et al.Rethinking Atrous Convolution for Semantic Image Segmentation//2017 Sachin Mehta,et al. ESPNet: Efficient Spatial Pyramid of DilatedConvolutions for Semantic Segmentation. //ECCV 2018 Tianyi Wu**,et al.Tree-structured Kronecker Convolutional Networks for Semantic Segmentation.//AAAI2019** Hyojin Park,et al.Concentrated-Comprehensive Convolutionsfor lightweight semantic segmentation.//2018 Efficient Smoothing of Dilated Convolutions for Image Segmentation.//2019
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