What is the difference between Multitask and Multiclass learning

Question

Consider a image labeling problem, where I need to assign one or more labels to an image. The possible labels are human, moving , indoor. Human means there is a human in the picture, moving could mean whether the human is runing/walking etc, and indoor classifies whether the image is indoor or outdoor.

Now I can train a DNN with 3 nodes in the output layer using labeled training data. (say node 1, is for +/- human, node 2 +/- moving and so on)

I always thought that this is just another way of doing multi-class classification using DNNs. (Another alternative for multi-class training would have been to train many DNNs with one output node in a pairwise or 1-vs-all fashion.)

But it seems like what I described (training a DNN with 3 output nodes) is Multi-task learning. ( I read wiki on MTL and looked at these papers)

My question is: is the scenario I described MTL? If not what am I missing? If yes, is there anything more to MTL? (clearly there is)

Also I notice in MTL a image can have multiple labels, but usually in multiclass an image is assigned only one - is this the difference?

Answer 1

Just to give a more clear understanding, I have explained each terminology with examples

• Multiclass classification/(One-Vs-One and One-Vs-ALL):

  • Classification task with >2 classes!
  • Assumption is that each sample is assigned to one and only one label
  • E.g. MNIST
  • E.g. a set of images of fruits which may be oranges, apples, or pears.
  • Last layer activation function would be softmax-softmax activation function generalizes the logistic activation function to C classes rather than just two classes.(single label to single example)

    

    • Eg saying each image is either pedestrians or car or detect stop signs!
    • Eg.saying each image is either of the numbers between 0-9(MNIST)!

  • Or you can use 4 different logistic regression classifers- each neuron in the last layer has sigmoid activation function (extension of one-vs-all method!)!

• Multilabel classification:

• This Classification task assigns a set of target labels to each sample.

• E.g. Building a classifier for a self-driving car that would need to detect several different things such as pedestrians, detect other cars, detect stop signs in an image!.

• E.g. A document could have multiple topics!

• Loss function used would be "logLoss"

• Multioutput regression/ Multitarget regression/multidimensional linear regression:

• This task assigns a sample to a set of target values!

• E.g.predicting several properties for each data-point, such as wind direction and magnitude at a certain location

• Last layer activation should be linear

• Loss function used would be "MSE"

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Now let's comes to the difference between multi-task learning(one subset is a multilabel classification or multioutput regression) and multiclass classification problem! :

• Multi-class classification: You are assigning a single label (could be multiple labels such as MNIST problem) to the input image as explained above.

• Multi-task learning (one subset of it is multi-label classification) You're asking for each picture, does it have a pedestrian, or a car a stop-sign or traffic-light, and multiple objects could appear in the same image (one image can have multiple labels!).

  • In other words, If you train a neural network to minimize this cost function (Log loss) you are carrying out multi-task learning because what you're doing is building a single neural network that is looking at each image and basically solving four different classification problems. It's trying to tell you does each image have each of these four objects in it?.

  • Or you could achieve it by just training four separate neural networks instead of train one network to do four things!

  • Note: But if some of the earlier features in the neural network can be shared between these different types of objects, then you find that training one NN to do four things, results in better performance than training four completely separate neural networks to do the four tasks separately. That's the power of Multi-task learning!

To put everything into perspective, Multi-task learning will make more sense when you have the below requirements:

• Training on a set of tasks that could benefit from having shared lower-level features!

• If the amount of data you have for each task is quite similar!

  @RockTheStar, I think your transfer learning suggestion isn't correct! What you are trying to say & have similar(but weak) meaning for transfer learning, I will try to explain with the help of an example. Suppose you have 100 different tasks and you have 1000 train examples for each! If you build you separate NN for classifying each class you won't do much good! due to a limited number of training example (1000) but if you build a single network for solving 100 different, let's say classification problem then, you have (1000*1000)(Million instead of just 1000 for each separate task) training examples that can provide some knowledge that helps every other task among these 100 tasks. This is a big boost!

• Can train a big enough NN to do well on all the tasks!

Answer 2

Slight correction on @RockTheStar answer. Multi-task learning is not when you learn for one task and then transfer to another as was suggested, instead the tasks are learned in parallel similar to the usual multi-class classification setup.

I suppose the simple distinction that can be made is that the outputs are not necessarily classes of the same of a single task, but two or more loosely related tasks that are sharing information.

Answer 3

Multi-class learning: have multiple class labels that you want to classify. For example, I have labels cat, dog, and pig. These are animals. I want to make a DNN to classify them. That's a multi-class learning.

Multi-task learning: is somewhat like transfer learning. Basically, you create one model for one classification task and them use it in another classification task (after modification).

In you case, you can create a two-label DNN to classify if the image is human or not. Then use that model parameters parameters to start to build another classifier that classify moving or not. I will consider your case more like multi-task learning because the subjects of interests are different.

Answer 4

I notice in MTL a image can have multiple labels, but usually in multiclass an image is assigned only one - is this the difference?`

Yes.

In multi-task learning, you can detect multiple objects (i.e. 'car', 'human', 'cat', 'tree') in one given image. If there exist both a human and a car in your model, Your labels are [1,1,0,0] (from ['car', 'human', 'cat', 'tree']).

In multiclass classification, you will have only one gold (1) in your labels, [1,0,0,0].

Answer 5

There is quite some information in all of the answers provided thus far, but maybe a summary answer might be useful for people with the same / a similar question:

• Multi-class learning is the terminology used when you predict a single label for each input, but each label is a single element from a set of possible labels. Taking the example from this answer: you have an image of an animal and the goal is to label whether it is a cat OR a dog OR a pig. It can only be one of these animals, but there are multiple options. To train a network on this kind of problems, you would generally use the Cross-entropy loss function.

• Multi-task learning is when you have different problems that need to be solved simultaneously (as stated in this answer. Each problem could be a binary classification problem (as the OP example) or another multi-class problem. Taking the example of the animal images again: a second task could be to predict whether the environment on the picture is sunny OR cloudy OR indoor. So now the goal is to find the animal label AND the environment label. The key idea is that solving one task could help solving the second task (in this example: cats are more likely to appear indoors than pigs). For training a network in this case, each output can have its own loss function. The loss functions can be combined by summing them up, using weights to balance the importance of each task.

• Multi-label learning (as mentioned in this answer) can be considered a special case of multi-task learning. In this case, there could be one or more animals in the image and you want a label for every animal in the image. In practice this is solved by breaking down this task in multiple binary problems. If the possible labels would be dog, cat and pig, the binary problems would be (1) Is there a dog in the image, (2) Is there a cat in the image, (3) is there a pig in the image. These sub-problems can then be tackled by multi-task methods.

Note that I ignored regression as a whole, but multi-task learning could also combine classification and regression problems.

Answer 6

The glossary from scikit-learn gave a very detailed yet overlapped meaning of these terms. I suggest the reader to read Anu's answer first, then come back to this answer. Note the equivalent terms (bold text) in each definition below.

continuous multioutput; continuous multi-output; multioutput continuous; multi-output continuous

A regression problem where each sample’s target consists of n_outputs outputs, each one a finite floating point number, for a fixed int n_outputs > 1 in a particular dataset.

Continuous multioutput targets are represented as multiple continuous targets, horizontally stacked into an array of shape (n_samples, n_outputs).

multiclass; multi-class

A classification problem consisting of more than two classes. A multiclass target may be represented as a 1-dimensional array of strings or integers. A 2d column vector of integers (i.e. a single output in multioutput terms) is also accepted.

multiclass multioutput; multi-class multi-output; multioutput multiclass; multi-output multi-class

A classification problem where each sample’s target consists of n_outputs outputs, each a class label, for a fixed int n_outputs > 1 in a particular dataset. Each output has a fixed set of available classes, and each sample is labeled with a class for each output. An output may be binary or multiclass, and in the case where all outputs are binary, the target is multilabel.

Multiclass multioutput targets are represented as multiple multiclass targets, horizontally stacked into an array of shape (n_samples, n_outputs).

multilabel; multi-label

A multiclass multioutput target where each output is binary.

multioutput; multi-output

A target where each sample has multiple classification/regression labels.

Source: https://scikit-learn.org/stable/glossary.html#term-continuous-multioutput