Welcome everybody to deep learning. So today we want to conclude talking about different

regularization methods and we want to talk in particular about one more technique that

is called multitask learning. Multitask learning, we want to extend the previous concept. So

previously we only had one network for one task and then we had transfer learning to

reuse the network. But the question is can we do more? Can we do it in a better way?

And there are some real world examples. For example, if you learn to play the piano and

the violin, then in both tasks you require good hearing, sense of rhythm, music notation

and so on. So there are some things that can be shared. Or also soccer and basketball training.

Both require stamina, speed, body awareness, body-eye coordination. So if you learn the

one, then you typically also have benefits for the other. So this would be even better

than reusing. So you learn simultaneously and then provide a better understanding of

the shared underlying concepts. So the idea now is that we train and network simultaneously

on multiple related tasks. So we adapt the loss function to assess performance for multiple

tasks and this then gives a multitask learning that introduces a so-called inductive bias.

We prefer a model that can explain more than a single task. Also this reduces the risk

of overfitting on one particular task and our model generalizes better. So let's look

at the setup. So we have some shared input layers. So these are like the feature extraction

layers and the representation layers. And then we split at some point where we go into

task specific layers and then evaluate it on task A, task B, task C. And they may be

very different but somehow related because otherwise it wouldn't make sense to share

the previous layers. So several hidden layers are shared between all of the tasks. And as

already shown by Baxter in 97, multitask learning of n tasks reduces the chance of overfitting

by an order of n. Instead of hard sharing you can also do soft parameter sharing. Soft

parameter sharing would now introduce an additional loss. So you constrain the activations in

the particular layers to be similar. So each model has its own parameters but we somehow

link them together to perform similar extraction steps yet different extraction steps in the

constrained layers. And you can do that for example with an L2 norm or other norms that

make them similar. Now we still have to talk about the auxiliary tasks. So all of these

tasks should have an own purpose. You may also just include auxiliary tasks just because

you want to create a more stable network. So one example here is facial landmark detection

by Zhang and they essentially want to detect facial landmarks but this is impeded by occlusion

and pose variances. So they start simultaneously to learn landmarks and subtly related tasks

like the face pose, smiling, not smiling, glasses, no glasses, occlusion and gender.

So they had this information available and then you can set this up in a multitask learning

framework as you see here in the network architecture. And in the results you see that they then

have the auxiliary tasks here but they're actually interested in the facial landmarks

and they compare this to a CNN, a cascaded CNN and now their multitask network with the

auxiliary tasks and they can show that also the landmark detection is improved by introduction

of these auxiliary tasks. So certain features may be difficult to learn for one task but

it may be easier for a related one. So the auxiliary tasks can help to steer the training

in a specific direction and we somehow include prior knowledge by choosing appropriate auxiliary

tasks. And of course then tasks can have different convergence rates so you can then also introduce

task-based early stopping. An open research question is what tasks are appropriate auxiliary

tasks? So this is something we cannot generally recommend but is typically determined by experimental

validation. So next time on deep learning we start with a new blog where we look into

some practical recommendations to actually make things work. So with all you've seen

you're already pretty far but there's a couple of hints that will make your life easier so

definitely recommend watching the next couple of lectures and we will look into how to evaluate

performance and deal with the most common problems that essentially everybody has to

face in the beginning and we also look at concrete case studies with all the pieces