Answers.Body: <p>Though metaphorically you're right to claim that contrastive learning objective is implicitly achieved by a supervised classifier in its intermediate or deeper layer(s) which reflects class similarity as you roughly reasoned, it's not necessarily always and exactly so.</p>
  <p>The semi-supervised contrastively learned embeddings are typically more general purpose and task agnostic based on their explicit semantic similarity encoding, suitable for transfer learning to downstream tasks and applicable to both labeled and unlabeled data using augmentation training such as SimCLR framework or clustering induced pseudo-labeling to define positive and negative pairs.</p>
  <p>On the other hand, the classifier trained embeddings are task specific and may not generalize well to other tasks other than classification. If samples from the same class exhibit significant variability, a classifier may focus on the decision boundary rather than reducing intra-class variance, while contrastive learning would explicitly reduce this variance.</p>
  
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Answers.CreationDate: 2024-11-24T03:08:58.263Z
Answers.LastActivityDate: 2024-11-24T03:08:58.263Z
Answers.UserDisplayName: cinch
Body: <p>Contrastive Learning learns representations of data such that similar samples (positive samples) are close to each other in an Euclidean space, and dissimilar ones (negative samples) are further from each other. If we have labeled samples, then we can easily get these positive and negative samples (by selective samples from the same label as positive samples).</p>
  <p>Is this implicitly achieved by a classifier in its intermediate layers?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-24T00:35:42.740Z
Comments.Text: hard to draw similarity between the two without formalizing the question. However, you might draw some similarity if you assume the first N-1 layers of a classifier as a sort of contrastive network, though from the loss perspective, I'm not sure you can derive much, but i might be wrong
Comments.UserDisplayName: Alberto
Tags: <classification><feedforward-neural-networks><contrastive-learning>
Title: Relation of Contrastive Learning and intermediate layers of classifier
UserDisplayName: Liubove
ThreadText: # Relation of Contrastive Learning and intermediate layers of classifier
  
  (Asked by: username_0 on 2024-11-23)
  
  Contrastive Learning learns representations of data such that similar samples (positive samples) are close to each other in an Euclidean space, and dissimilar ones (negative samples) are further from each other. If we have labeled samples, then we can easily get these positive and negative samples (by selective samples from the same label as positive samples).
  
  Is this implicitly achieved by a classifier in its intermediate layers?
  
  **Tags:** classification, feedforward-neural-networks, contrastive-learning
  
  > **Comments:**
  > **username_1**: hard to draw similarity between the two without formalizing the question. However, you might draw some similarity if you assume the first N-1 layers of a classifier as a sort of contrastive network, though from the loss perspective, I'm not sure you can derive much, but i might be wrong  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-11-24. Score: 0 | Accepted answer
  
  Though metaphorically you're right to claim that contrastive learning objective is implicitly achieved by a supervised classifier in its intermediate or deeper layer(s) which reflects class similarity as you roughly reasoned, it's not necessarily always and exactly so.
  
  The semi-supervised contrastively learned embeddings are typically more general purpose and task agnostic based on their explicit semantic similarity encoding, suitable for transfer learning to downstream tasks and applicable to both labeled and unlabeled data using augmentation training such as SimCLR framework or clustering induced pseudo-labeling to define positive and negative pairs.
  
  On the other hand, the classifier trained embeddings are task specific and may not generalize well to other tasks other than classification. If samples from the same class exhibit significant variability, a classifier may focus on the decision boundary rather than reducing intra-class variance, while contrastive learning would explicitly reduce this variance.
  
  
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