Answers.Body: <p>It's of course possible to reduce dimension using attention mechanisms, for instance by just setting a lower output dimension.  However, it's not obvious in the general case, as to why the processing provided by an attention layer should provide a good trade-off compared to other sorts of processing that can reduce dimension (like an ordinary dense layer plus activation).</p>
  <p>Ordinarily, and perhaps almost exclusively in current research, attention is used with some idea of gathering or reorganizing information across sequence, time or space, or between different sources or types of information such as in cross attention.  This might be because the similarity scores after projections could be just about anything.  If you were to divide up data between different chunks, it could provide a weighting of the chunks.  Depending on your data, perhaps this could be helpful, but so could a more ordinary approach.</p>
  <p>A quick search was able to find a reference to a paper which uses cross attention to help with dimensionality reduction and refers to it as <a href="https://paperswithcode.com/method/attention-feature-filters" rel="nofollow noreferrer">Attention Feature Filters</a>.  However, it should be noted that a separate feature is used to inform whether or not a feature is relevant.</p>
  <p>To summarize, feel free to experiment, but I would suggest first trying a more simple baseline approach unless there's additional data to inform the attention weights.</p>
  
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Body: <p>Is it possible to use attention layer as a sort of filter of input data before passing it further to the network?</p>
  <p>Is it possible to use it to reduce the dimension of the input (similar as PCA, for example - only attention layer would be trained with the network) and therefore reduce the network architecture (for example we can use network which accepts smaller input dimension)?</p>
  <p>I can't find decisive sources on that matter.</p>
  
Tags: <attention><dimensionality-reduction>
Title: Is it possible to use attention layer as a sort of filter of input data before passing it further to the network?
UserDisplayName: janek nowaczek
ThreadText: # Is it possible to use attention layer as a sort of filter of input data before passing it further to the network?
  
  (Asked by: username_0 on 2024-06-16)
  
  Is it possible to use attention layer as a sort of filter of input data before passing it further to the network?
  
  Is it possible to use it to reduce the dimension of the input (similar as PCA, for example - only attention layer would be trained with the network) and therefore reduce the network architecture (for example we can use network which accepts smaller input dimension)?
  
  I can't find decisive sources on that matter.
  
  **Tags:** attention, dimensionality-reduction
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-06-18. Score: 0
  
  It's of course possible to reduce dimension using attention mechanisms, for instance by just setting a lower output dimension. However, it's not obvious in the general case, as to why the processing provided by an attention layer should provide a good trade-off compared to other sorts of processing that can reduce dimension (like an ordinary dense layer plus activation).
  
  Ordinarily, and perhaps almost exclusively in current research, attention is used with some idea of gathering or reorganizing information across sequence, time or space, or between different sources or types of information such as in cross attention. This might be because the similarity scores after projections could be just about anything. If you were to divide up data between different chunks, it could provide a weighting of the chunks. Depending on your data, perhaps this could be helpful, but so could a more ordinary approach.
  
  A quick search was able to find a reference to a paper which uses cross attention to help with dimensionality reduction and refers to it as [Attention Feature Filters](https://paperswithcode.com/method/attention-feature-filters). However, it should be noted that a separate feature is used to inform whether or not a feature is relevant.
  
  To summarize, feel free to experiment, but I would suggest first trying a more simple baseline approach unless there's additional data to inform the attention weights.
  
  
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