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
SNNs are really cool: their goal is to create neural networks in which, if the input of any layer is normally distributed, the output will automatically also be normally distributed. This is amazing because normalizing the output of layers is known to be a very efficient way to improve the performance of neural networks, but the current ways to do it (eg BatchNorm) basically involve weird hacks, while in SNN the normalization is an intrinsic part of the mathematics of the neural net.
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A report from Gizmodo says that Google is partnering with the United States Department of Defense and building drone software. The project will reportedly apply Google’s usual machine learning prowess to identify objects in drone footage. Google’s involvement in the project wasn’t public, but it was apparently discussed internally at Google last week and leaked.
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