Body: <p>Given a supervised problem with X, y input pairs, one can do two things for obtaining the function f that maps X with y with Neural Networks (and in general in machine learning):</p>
  <ul>
  <li><p>Deploy directly a supervised learning algorithm that maps X to y</p>
  </li>
  <li><p>Deploy a (variational) auto-encoder for learning useful features, and then using these for training the supervised learning algorithm</p>
  </li>
  </ul>
  <p>I would like to be pointed to some papers/blogs that explain which technique is better and when or where they conduct empirical benchmarking experiments.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-09T10:18:02.733Z
Comments.Text: Are you asking about the advantages of using auto-encoders to pre-train models in an unsupervised way that are later trained in a supervised way to solve a classification task? You mention variational auto-encoders. Are you interested specifically in VAEs or any AE?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-12-09T10:35:12.333Z
Comments.Text: Yes exactly, AE or VAE is the same
Comments.UserDisplayName: Tommaso Bendinelli
Tags: <classification><reference-request><autoencoders><supervised-learning><transfer-learning>
Title: Literature on the advantages of using an auto-encoder for classification
UserDisplayName: Tommaso Bendinelli
ThreadText: # Literature on the advantages of using an auto-encoder for classification
  
  (Asked by: username_0 on 2020-12-09)
  
  Given a supervised problem with X, y input pairs, one can do two things for obtaining the function f that maps X with y with Neural Networks (and in general in machine learning):
  
  * Deploy directly a supervised learning algorithm that maps X to y
  * Deploy a (variational) auto-encoder for learning useful features, and then using these for training the supervised learning algorithm
  
  I would like to be pointed to some papers/blogs that explain which technique is better and when or where they conduct empirical benchmarking experiments.
  
  **Tags:** classification, reference-request, autoencoders, supervised-learning, transfer-learning
  > **Comments:**
  > **username_1**: Are you asking about the advantages of using auto-encoders to pre-train models in an unsupervised way that are later trained in a supervised way to solve a classification task? You mention variational auto-encoders. Are you interested specifically in VAEs or any AE?  
  > **username_0**: Yes exactly, AE or VAE is the same  
  
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  ## 0 answers
  
  