Body: <p>I am asking this question on deep neural network architectures only. If you want to restrict the domain of tasks then you can choose computer vision for this question.</p>
  <p>Suppose there is an architecture that performs well on a task. Is it possible can edit or append the first or last few layers and then it performs similarly well on the other task?</p>
  <p>If yes, please provide me an example of such architecture that performs well on at least a couple of tasks.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T13:46:06.390Z
Comments.Text: It seems that you're looking for something similar to [_transfer learning_](https://www.tensorflow.org/tutorials/images/transfer_learning). Let me know if that's the case or not.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T22:13:25.630Z
Comments.Text: A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model **to a given task**. #1/#2
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T22:14:01.620Z
Comments.Text: Fine-Tuning: Unfreeze a few of the top layers of a frozen model base and jointly train both the newly-added classifier layers and the last layers of the base model. This allows us to "fine-tune" the higher-order feature representations in the base model **in order to make them more relevant for the specific task**. #2/#2
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T22:15:10.170Z
Comments.Text: Yeah @nbro. It is the same-thing I am looking for. It seems that task is not fixed and hence we can use pre-trained model for any task under consideration.
Comments.UserDisplayName: hanugm
Tags: <deep-learning><architecture>
Title: Is it possible that a deep neural network, with some variations, can be used for multiple tasks?
UserDisplayName: hanugm
ThreadText: # Is it possible that a deep neural network, with some variations, can be used for multiple tasks?
  
  (Asked by: username_0 on 2022-01-04)
  
  I am asking this question on deep neural network architectures only. If you want to restrict the domain of tasks then you can choose computer vision for this question.
  
  Suppose there is an architecture that performs well on a task. Is it possible can edit or append the first or last few layers and then it performs similarly well on the other task?
  
  If yes, please provide me an example of such architecture that performs well on at least a couple of tasks.
  
  **Tags:** deep-learning, architecture
  
  > **Comments:**
  > **username_1**: It seems that you're looking for something similar to [_transfer learning_](https://www.tensorflow.org/tutorials/images/transfer_learning). Let me know if that's the case or not.  
  > **username_0**: A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model **to a given task**. #1/#2  
  > **username_0**: Fine-Tuning: Unfreeze a few of the top layers of a frozen model base and jointly train both the newly-added classifier layers and the last layers of the base model. This allows us to "fine-tune" the higher-order feature representations in the base model **in order to make them more relevant for the specific task**. #2/#2  
  > **username_0**: Yeah @username_1. It is the same-thing I am looking for. It seems that task is not fixed and hence we can use pre-trained model for any task under consideration.  
  
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  ## 0 answers
  
  