Body: <p>I want to use a hypernetwork on an entire vision backbone (39m parameters).</p>
  <p>The hypernetwork structure looks like:</p>
  <p>512 -&gt; 512 -&gt; 512 -&gt; 39m</p>
  <p>Unfortunately, the last layer means the hypernetwork has billions of parameters.</p>
  <p>What could I do to reduce the parameter count of the hypernetwork?</p>
  <p>(The 512-dimensional input vector is a noise vector that acts like a key that describes what details to focus on).</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-28T21:24:24.783Z
Comments.Text: why do you need a hypernetwork for such a large model? what kind of task are you trying to solve?
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-29T00:22:48.470Z
Comments.Text: 2 convnet encoders for multimodal embedding and 1 decoder for one of the modalities. The entire network is conditioned by a noise vector for embedding diversity under uncertainty.
Comments.UserDisplayName: Richie Bendall
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-10-29T10:38:48.990Z
Comments.Text: just pass the noise as part of the input
Comments.UserDisplayName: Alberto
Tags: <network-design>
Title: Hypernetwork too many parameters problem
UserDisplayName: Richie Bendall
ThreadText: # Hypernetwork too many parameters problem
  
  (Asked by: username_0 on 2024-10-28)
  
  I want to use a hypernetwork on an entire vision backbone (39m parameters).
  
  The hypernetwork structure looks like:
  
  512 -> 512 -> 512 -> 39m
  
  Unfortunately, the last layer means the hypernetwork has billions of parameters.
  
  What could I do to reduce the parameter count of the hypernetwork?
  
  (The 512-dimensional input vector is a noise vector that acts like a key that describes what details to focus on).
  
  **Tags:** network-design
  > **Comments:**
  > **username_1**: why do you need a hypernetwork for such a large model? what kind of task are you trying to solve?  
  > **username_0**: 2 convnet encoders for multimodal embedding and 1 decoder for one of the modalities. The entire network is conditioned by a noise vector for embedding diversity under uncertainty.  
  > **username_1**: just pass the noise as part of the input  
  
  ---
  
  ## 0 answers
  
  