Inception Network Explained

Key Takeaways

hi thanks for checking out Henry AI Labs this video is going to cover the inception network the fundamental idea behind the inception network is the inception block in a traditional neural network layer or convolutional neural network layer you take the output from previous layer and that would be the input to the next layer and it would follow that pattern all the way until the prediction but the inception block takes apart the individual layers and instead of just passing it through one layer it takes the previous layer input and passes it to four different operations in parallel and then concatenates the outlets from all these different layers there's a pretty simple idea to comprehend an image a image B looks more complex but the fundamental idea is that they add these one-by-one convolutions just to shrink the filter the depth of the feature map so like a one by one convolution preserves it spatially but you can use that parameter where you say how many filters you want to use and that can lower the dimension so that you have less of a computational cost for this so another interesting idea in the inception network paper is this idea of intermediate classifiers to solve vanishing gradient problems so on the left is an image of the inception Network zoomed out and then on the right is to illustrate these intermediate classifiers which are the yellow blocks so this is kind of an idea that is seen in multi task learning where there is a shared feature extraction networking and there's these different heads that do different tasks but in this case they all do the same task and they have like increasing complexity like the first classifier is essentially branched right off with a shared feature of representations and then the next one has like three inception blocks before the classifier and so on so what they do is so this is a mechanism they used to solve the vanishing gradient like as the grading is back propagated all the way to the initial layers it it comes really small and they hardly update the weights so they use these intermediate classifiers and they somewhat like nerf the magnitude of the loss on the inner me classifiers to normalize the update so the inception block and the intermediate classifiers are really the two main ideas behind this network it said the state of the art when it was released and if you want to see more details about it please check out the article on Henry AI lives calm