<blockquote>
<p>Is there any fundamental difference between the two?</p>
</blockquote>
<p>The difference is in the names:</p>
<ul>
<li>Multi task means that we are learning more than a single task, i.e. the labels we have will be used to compute different losses</li>
<li>Multi label means that for a single task, more than one label is allowed as correct prediction. but in practice, the different labels will be used to compute a single loss.</li>
</ul>
<p>You can also visualize the difference: a network trained on multi tasks will have more output layers, one per task,whereas a network trained on multi label classification require only one output layer. Note that in a multi task learning setting one of the tasks could very well be multi label classification.</p>
<blockquote>
<p>I am confused if I should consider it as a multi-class multilabel data or a multi-class MTL kind of an approach</p>
</blockquote>
<p>That depends on what you're trying to do, and we can't even give suggestions without knowing the data.</p>
<img src="https://i.sstatic.net/i5c93.png" width="500" height="400" />

CC BY-SA 4.0
2022-03-12T01:19:25.137Z
Thanks Edoardo for the explanation. To be specific, I have 3 sec speech audio inputs and labels which state if the particular audio input has sound repetition,  prolongation and other types of speech disfluencies. And each disfluency is assigned a number between 0-3. A given sample can be categorized as sound repetition =2 and prolongation=1. So I think with an objective to classify the audio inputs into different classes of disfluency I can either choose a single task learning or multi task, I think.
Payal Mohapatra
CC BY-SA 4.0
2022-03-11T00:23:07.900Z
2022-03-11T10:24:04.420Z
2022-03-11T10:24:04.420Z
Edoardo Guerriero
<p>I am working on a data-set that has multiple labels associated with it (not necessarily independent of each other). During my development, I am confused if I should consider it as a multi-class multilabel data or a multi-class MTL kind of an approach. Is there any fundamental difference between the two?</p>

CC BY-SA 4.0
2022-03-10T23:07:38.173Z
Hello. "MTL" stands for? Multi-task learning?
nbro
<multi-label-classification><multi-task-learning>
What is the difference between multi-label and multi-task classification?
Payal Mohapatra
# What is the difference between multi-label and multi-task classification?

(Asked by: username_0 on 2022-03-10)

I am working on a data-set that has multiple labels associated with it (not necessarily independent of each other). During my development, I am confused if I should consider it as a multi-class multilabel data or a multi-class MTL kind of an approach. Is there any fundamental difference between the two?

**Tags:** multi-label-classification, multi-task-learning

> **Comments:**
> **username_1**: Hello. "MTL" stands for? Multi-task learning?  

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## 1 answers

### Answer by username_2 on 2022-03-11. Score: 1

> Is there any fundamental difference between the two?

The difference is in the names:

* Multi task means that we are learning more than a single task, i.e. the labels we have will be used to compute different losses
* Multi label means that for a single task, more than one label is allowed as correct prediction. but in practice, the different labels will be used to compute a single loss.

You can also visualize the difference: a network trained on multi tasks will have more output layers, one per task,whereas a network trained on multi label classification require only one output layer. Note that in a multi task learning setting one of the tasks could very well be multi label classification.

> I am confused if I should consider it as a multi-class multilabel data or a multi-class MTL kind of an approach

That depends on what you're trying to do, and we can't even give suggestions without knowing the data.

![](https://i.sstatic.net/i5c93.png)

> **Comments:**
> **username_0**: Thanks Edoardo for the explanation. To be specific, I have 3 sec speech audio inputs and labels which state if the particular audio input has sound repetition,  prolongation and other types of speech disfluencies. And each disfluency is assigned a number between 0-3. A given sample can be categorized as sound repetition =2 and prolongation=1. So I think with an objective to classify the audio inputs into different classes of disfluency I can either choose a single task learning or multi task, I think.  

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