Welcome back to deep learning. Today we want to look a bit more into visualization techniques

and in particular the gradient based and optimization based procedures. Okay so let's

say what I've got for you. Let's talk first about the gradient based visualizations and here the

idea is that we want to figure out which input pixel is most significant to a

neuron and if we would change it what would cause a large variation in the

actual output of our neural network. So what we actually want to compute is the

partial derivative of the neuron under consideration maybe an output neuron

like for the class cat and then we want to compute the partial derivative with

respect to the respective input and this is essentially back propagation through

the entire network and then we can visualize this gradient as type of image

which we have been doing here for this cat image and you can see that of course

this is a color gradient you see that this is a bit of a noisy image but you

can see that what is related to cat here is obviously also located in the area

where the cat is actually located in the image. So we will learn several different

approaches to do this and the first one is based here on reference number 20. For

back propagation we actually need a loss what we want to back propagate and we

simply take up pseudo loss that is simply the activation of an arbitrary

neuron or layer and typically what you want to do is you want to take neurons

in the output layer because they can be associated to a class and what you can

also do is instead of using back propagation you can build a nearly

equivalent alternative which uses a kind of reverse network and this is the

dconfnet from reference and 26 so here the input is the trained network and

some image then you choose one activation and set all of the other

activations to zero then you build a reverse network and you can see the idea

here that this is essentially containing the same as the network but just in

reverse sequence with so-called unpooling steps and now with these

unpooling steps and the reverse computation you can see that we can also

produce a kind of gradient estimate. The nice thing about this one is there's no

training involved so you just have to record the pooling location in the

switches and the forward pass of the reverse network effectively is the same

as the backward pass of the network apart from the rectified linear units

which we will look at in a couple of slides and here we show the

visualizations of the top nine activations the gradient and the

corresponding patch so for example you can reveal with this one that this kind

of feature map seems to focus on green patchy areas and you could argue that

this is more a kind of background feature that tries to detect grass

patches in the image. So what else? Well there's guided back propagation and

guided back propagation is a very similar concept and the idea here is

that you want to find positively correlated features so we are looking

for positive gradients because we assume that the features that are positive are

the ones that the neuron is interested in and the negative gradients are the

ones that the neuron is not interested in. So the idea is then to set all

negative gradients in the back propagation to zero and we can show you

now the different processes of the relu during the forward and backward passes

with the different kinds of gradient back propagation techniques. Well of

course if you have this input activations then in the forward pass in

the relu you would simply cancel out all the negative values and set them to zero.

Now what happens in the back propagation for the three different

alternatives? Let's look at what the typical back propagation does and note

that we show here the negative entries that came from the sensitivity in yellow