So from this paper. “Multi-Scale Context Aggregation by Dilated Convolutions”, I was introduced to Dilated Convolution Operation. And to be honest it is just convolution operation with modified kernel, to be exact, wider kernel.
However, to understand something fully, I need to implement it, hence the existence of this post.
Source: towardsdatascience.com
Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+ [1], implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture [2, 3] for the most accurate results, intended for server-side deployment. As part of this release, we are additionally sharing our Tensorflow model training and evaluation code, as well as models already pre-trained on the Pascal VOC 2012 and Cityscapes benchmark semantic segmentation tasks.
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Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space.
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