Answers.Body: <p>You are probably looking for <strong>incremental learning</strong> (sometimes known as <em>lifelong learning</em>) techniques, i.e. machine learning techniques that attempt to address the <a href="https://ai.stackexchange.com/a/13293/2444"><strong>catastrophic forgetting</strong></a> effect of neural networks when trained incrementally, i.e. as new classes or data are added to the original training data.</p>
  <p>There are different techniques and some of them store (or compress) the old data in order to fully or partially re-train the neural network with the new classes or data.  However, note that this is a relatively new area of research and significant progress still needs to be made to produce serious tools. If you are specifically interested in incremental class learning, maybe have a look at this paper <a href="https://arxiv.org/pdf/1903.07864.pdf" rel="nofollow noreferrer">Class-incremental Learning via Deep Model Consolidation</a> (2020).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-08-18T14:48:53.567Z
Answers.LastActivityDate: 2020-08-18T14:48:53.567Z
Answers.UserDisplayName: nbro
Body: <p>Suppose you have a ground plane and can use a stereo vision system to detect things that are possibly separate objects.</p>
  <p>Suppose also your robot or agent can attempt to pick up and move these objects around in real-time.</p>
  <p>Is there any current system in computer vision that allows new objects to be learned in real-time?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-15T13:25:04.753Z
Comments.Text: By "real-time" do you mean "incrementally", i.e. learn new classes incrementally as they arrive, and not necessarily strictly "online" and "real-time" (i.e. fast)?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-08-15T14:22:11.063Z
Comments.Text: Both possibly, basically not require retraining of the whole system.
Comments.UserDisplayName: FourierFlux
Tags: <machine-learning><computer-vision><online-learning><incremental-learning>
Title: Is there any real-time computer vision system that can learn to detect new objects of new classes?
UserDisplayName: FourierFlux
ThreadText: # Is there any real-time computer vision system that can learn to detect new objects of new classes?
  
  (Asked by: username_0 on 2020-08-13)
  
  Suppose you have a ground plane and can use a stereo vision system to detect things that are possibly separate objects.
  
  Suppose also your robot or agent can attempt to pick up and move these objects around in real-time.
  
  Is there any current system in computer vision that allows new objects to be learned in real-time?
  
  **Tags:** machine-learning, computer-vision, online-learning, incremental-learning
  > **Comments:**
  > **username_1**: By "real-time" do you mean "incrementally", i.e. learn new classes incrementally as they arrive, and not necessarily strictly "online" and "real-time" (i.e. fast)?  
  > **username_0**: Both possibly, basically not require retraining of the whole system.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-08-18. Score: 1
  
  You are probably looking for **incremental learning** (sometimes known as *lifelong learning*) techniques, i.e. machine learning techniques that attempt to address the [**catastrophic forgetting**](https://ai.stackexchange.com/a/13293/2444) effect of neural networks when trained incrementally, i.e. as new classes or data are added to the original training data.
  
  There are different techniques and some of them store (or compress) the old data in order to fully or partially re-train the neural network with the new classes or data. However, note that this is a relatively new area of research and significant progress still needs to be made to produce serious tools. If you are specifically interested in incremental class learning, maybe have a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf) (2020).
  
  
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