Answers.Body: <p>Few-short learning (FSL) can be useful for many (if not all) machine learning problems, including supervised learning (regression and classification) and reinforcement learning.</p>
  
  <p>The paper <a href="https://arxiv.org/pdf/1904.05046.pdf" rel="nofollow noreferrer">Generalizing from a Few Examples: A Survey on Few-Shot Learning</a> (2020) provides an overview (including examples of applications and use cases) of FSL. Their definition of FSL provided is based on Tom Mitchell's famous definition of machine learning.</p>
  
  <blockquote>
    <p>Definition 2.1 (<strong>Machine Learning</strong> [92, 94]). A computer program is said to learn from experience <span class="math-container">$E$</span> with respect to some classes of task <span class="math-container">$T$</span> and performance measure <span class="math-container">$P$</span> if its performance can improve with <span class="math-container">$E$</span> on <span class="math-container">$T$</span> measured by <span class="math-container">$P$</span>.</p>
  </blockquote>
  
  <p>Here's the definition of FSL.</p>
  
  <blockquote>
    <p>Definition 2.2. <strong>Few-Shot Learning</strong> (FSL) is a type of machine learning problems, specified by <span class="math-container">$E$</span>, <span class="math-container">$T$</span> and <span class="math-container">$P$</span>, where <span class="math-container">$E$</span> contains only a limited number of examples with supervised information for the task <span class="math-container">$T$</span>.</p>
  </blockquote>
  
  <p>Specific examples of applications of FSL are</p>
  
  <ul>
  <li>character generation</li>
  <li>drug toxicity discovery</li>
  <li>sentiment classification from short text</li>
  <li>object recognition</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-05-17T11:23:21.260Z
Answers.LastActivityDate: 2020-05-17T13:34:06.323Z
Answers.LastEditDate: 2020-05-17T13:34:06.323Z
Answers.UserDisplayName: nbro
Answers.Body: <p>An interesting use case is IQ tests, or program synthesis from examples in general.</p>
  
  <p>IQ tests often require you to derive a program from a few examples that can produce a certain output. See for instance <a href="https://github.com/fchollet/ARC" rel="nofollow noreferrer">https://github.com/fchollet/ARC</a></p>
  
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Answers.CreationDate: 2020-05-17T17:17:48.897Z
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Answers.UserDisplayName: apollo8
Body: <p>Besides computer vision and image classification, what other use cases/applications are for few-shot learning?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-17T06:35:04.243Z
Comments.Text: Anything? A system which can learn from a single example is the holy grail.
Comments.UserDisplayName: FourierFlux
Tags: <machine-learning><reference-request><applications><few-shot-learning>
Title: What are some use cases of few-shot learning?
UserDisplayName: etang
ThreadText: # What are some use cases of few-shot learning?
  
  (Asked by: username_0 on 2020-05-17)
  
  Besides computer vision and image classification, what other use cases/applications are for few-shot learning?
  
  **Tags:** machine-learning, reference-request, applications, few-shot-learning
  
  > **Comments:**
  > **username_1**: Anything? A system which can learn from a single example is the holy grail.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2020-05-17. Score: 3
  
  Few-short learning (FSL) can be useful for many (if not all) machine learning problems, including supervised learning (regression and classification) and reinforcement learning.
  
  The paper [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://arxiv.org/pdf/1904.05046.pdf) (2020) provides an overview (including examples of applications and use cases) of FSL. Their definition of FSL provided is based on Tom Mitchell's famous definition of machine learning.
  
  > Definition 2.1 (**Machine Learning** [92, 94]). A computer program is said to learn from experience $E$ with respect to some classes of task $T$ and performance measure $P$ if its performance can improve with $E$ on $T$ measured by $P$.
  
  Here's the definition of FSL.
  
  > Definition 2.2. **Few-Shot Learning** (FSL) is a type of machine learning problems, specified by $E$, $T$ and $P$, where $E$ contains only a limited number of examples with supervised information for the task $T$.
  
  Specific examples of applications of FSL are
  
  * character generation
  * drug toxicity discovery
  * sentiment classification from short text
  * object recognition
  
  
  ---
  
  ### Answer by username_3 on 2020-05-17. Score: 0
  
  An interesting use case is IQ tests, or program synthesis from examples in general.
  
  IQ tests often require you to derive a program from a few examples that can produce a certain output. See for instance <https://github.com/fchollet/ARC>
  
  
  ---
  
  