Body: <p>I am currently designing a Model which takes Input 3D Grid and Model Output at <span class="math-container">$t-1$</span>. The model figure is described below</p>
  <p><a href="https://i.sstatic.net/ThR6g.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ThR6g.png" alt="enter image description here" /></a></p>
  <p>I have two thoughts in training the model for above situation.</p>
  <ul>
  <li><p>Feed output <span class="math-container">$t-1$</span> from ground truth with some noise. And, during testing feed output of the model as previous output at <span class="math-container">$t-1$</span>. Maybe we can fine tune with model output when training loss is sufficiently low.</p>
  </li>
  <li><p>Feed model output to next stage as <span class="math-container">$t-1$</span> output. But I am not sure if this works.</p>
  </li>
  </ul>
  <p>The situation is similar to RNNs but I am using 3D CNN’s here. I don’t know if RNN can be used here. How can I train such a model.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-26T14:26:51.353Z
Comments.Text: What is the word looking like **shoref** in between the two 3D-Encoders?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-26T14:42:16.977Z
Comments.Text: Sorry, It is "shared" not shoref. My bad. These are shared convolutions.
Comments.UserDisplayName: Rajat
Tags: <deep-learning><convolutional-neural-networks><training><recurrent-neural-networks><pytorch>
Title: Feeding the output back to input in 3D CNN model
UserDisplayName: Rajat
ThreadText: # Feeding the output back to input in 3D CNN model
  
  (Asked by: username_0 on 2021-07-26)
  
  I am currently designing a Model which takes Input 3D Grid and Model Output at $t-1$. The model figure is described below
  
  [![enter image description here](https://i.sstatic.net/ThR6g.png)](https://i.sstatic.net/ThR6g.png)
  
  I have two thoughts in training the model for above situation.
  
  * Feed output $t-1$ from ground truth with some noise. And, during testing feed output of the model as previous output at $t-1$. Maybe we can fine tune with model output when training loss is sufficiently low.
  * Feed model output to next stage as $t-1$ output. But I am not sure if this works.
  
  The situation is similar to RNNs but I am using 3D CNN’s here. I don’t know if RNN can be used here. How can I train such a model.
  
  **Tags:** deep-learning, convolutional-neural-networks, training, recurrent-neural-networks, pytorch
  > **Comments:**
  > **username_1**: What is the word looking like **shoref** in between the two 3D-Encoders?  
  > **username_0**: Sorry, It is "shared" not shoref. My bad. These are shared convolutions.  
  
  ---
  
  ## 0 answers
  
  