Answers.Body: <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" />
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-03-12T01:19:25.137Z
Answers.Text: 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.
Answers.UserDisplayName: Payal Mohapatra
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-03-11T00:23:07.900Z
Answers.LastActivityDate: 2022-03-11T10:24:04.420Z
Answers.LastEditDate: 2022-03-11T10:24:04.420Z
Answers.UserDisplayName: Edoardo Guerriero
Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-10T23:07:38.173Z
Comments.Text: Hello. "MTL" stands for? Multi-task learning?
Comments.UserDisplayName: nbro
Tags: <multi-label-classification><multi-task-learning>
Title: What is the difference between multi-label and multi-task classification?
UserDisplayName: Payal Mohapatra
ThreadText: # 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?  
  
  ---
  
  ## 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 username_2 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.  
  
  ---
  
  