Answers.Body: <p>I believe the standard meanings are as follows, but not everyone uses words in the same way, so you might see examples that differ.</p>
  <p><strong>Fine tuning</strong> refers to slightly changing the weights of a pre-trained model (i.e. using a low learning rate) to adapt to a specific task.</p>
  <p><strong>Few shot learning</strong> refers to using a very small dataset to adapt to a specific task.</p>
  <p>Someone might do both at the same time (fine-tuning with a small dataset), just fine-tuning (with a big dataset), or just few shot learning (e.g. giving GPT a few examples to prompt a particular kind of answer).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-15T01:15:52.903Z
Answers.LastActivityDate: 2022-06-15T01:15:52.903Z
Answers.UserDisplayName: Lee Reeves
Body: <p>I am trying to understand the concept of fine-tuning and few-shot learning.</p>
  <p>I understand the need for fine-tuning. It is essentially tuning a pre-trained model to a specific downstream task.</p>
  <p>However, recently I have seen a plethora of blog posts stating zero-shot learning, one-shot learning and few-shot learning.</p>
  <p>How are they different from fine-tuning? It appears to me that few-shot learning is a specialization of fine-tuning. What am I missing here?</p>
  <p>Can anyone please help me?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-14T10:47:32.380Z
Comments.Text: @nbro no. They don't discuss about few shot learning and their difference.
Comments.UserDisplayName: Exploring
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-14T14:27:03.887Z
Comments.Text: Ok, then edit your post to explain that you're just interested in few-shot learning and why that other post doesn't answer your question.
Comments.UserDisplayName: nbro
Tags: <deep-learning><fine-tuning><pretrained-models><few-shot-learning><zero-shot-learning>
Title: What is the difference between fine tuning and variants of few shot learning?
UserDisplayName: Exploring
ThreadText: # What is the difference between fine tuning and variants of few shot learning?
  
  (Asked by: username_0 on 2022-06-14)
  
  I am trying to understand the concept of fine-tuning and few-shot learning.
  
  I understand the need for fine-tuning. It is essentially tuning a pre-trained model to a specific downstream task.
  
  However, recently I have seen a plethora of blog posts stating zero-shot learning, one-shot learning and few-shot learning.
  
  How are they different from fine-tuning? It appears to me that few-shot learning is a specialization of fine-tuning. What am I missing here?
  
  Can anyone please help me?
  
  **Tags:** deep-learning, fine-tuning, pretrained-models, few-shot-learning, zero-shot-learning
  
  > **Comments:**
  > **username_0**: @username_1 no. They don't discuss about few shot learning and their difference.  
  > **username_1**: Ok, then edit your post to explain that you're just interested in few-shot learning and why that other post doesn't answer your question.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-06-15. Score: 3
  
  I believe the standard meanings are as follows, but not everyone uses words in the same way, so you might see examples that differ.
  
  **Fine tuning** refers to slightly changing the weights of a pre-trained model (i.e. using a low learning rate) to adapt to a specific task.
  
  **Few shot learning** refers to using a very small dataset to adapt to a specific task.
  
  Someone might do both at the same time (fine-tuning with a small dataset), just fine-tuning (with a big dataset), or just few shot learning (e.g. giving GPT a few examples to prompt a particular kind of answer).
  
  
  ---
  
  