Answers.Body: <p>One of the tools I like for a problem like this is a random forest.  Because it uses both row (sample) and column (variable) bootstrapping, it tends to be robust and get around noise.</p>
  <p>If I had to attack this, I would use a random forest to start with, and confirm whether it was properly labeling outside the training set.  For images that takes a bit of preprocessing.  After it is up and working, I would distill it into something like a neural network that is more engineered to work with image data.</p>
  <p>Some clarifying questions:</p>
  <ul>
  <li>What can you tell us about rates of false labeling?  Is it a few percent per class, or a sizeable chunk?</li>
  <li>Are you constrained to a particular architecture, platform, or toolset?</li>
  <li>What more can you tell us about input and outputs?  Sizes?  Quantities?  etc.</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-20T18:19:58.190Z
Answers.LastActivityDate: 2024-05-20T18:19:58.190Z
Answers.UserDisplayName: EngrStudent
Body: <p>I have a dataset which consists of satellite images and their labels which are indicated by let say class 1 and 2.</p>
  <p>I want to perform image segmentation to detect pixels related to class 1 and 2. The main issue is that the labels for my dataset are obtained using another approach and they contain some noise. For instance, some pixels should have been labeled as class 1, but in fact they are labeled as background. I wanted to ask if there is any approach for training using noisy labels?
  As an idea I wanted to use a teacher and student approach:</p>
  <ol>
  <li>start with a subset of clean labels</li>
  <li>train a model using the clean labels.</li>
  <li>generate pseudo-labels and filter out high quality labels</li>
  <li>combine them with previously clean data</li>
  <li>repeat the training process until a good performance is obtained.</li>
  </ol>
  <p>The main problem with this approach is that it is difficult to collect a subset of clean labels to start from. I do have lots of labeled data which may be useful during training although they are noisy.</p>
  <p>Please suggest any useful approach and resource here.</p>
  
Tags: <computer-vision><image-segmentation><semi-supervised-learning>
Title: Image segmentation with noisy labels
UserDisplayName: Carol
ThreadText: # Image segmentation with noisy labels
  
  (Asked by: username_0 on 2024-03-08)
  
  I have a dataset which consists of satellite images and their labels which are indicated by let say class 1 and 2.
  
  I want to perform image segmentation to detect pixels related to class 1 and 2. The main issue is that the labels for my dataset are obtained using another approach and they contain some noise. For instance, some pixels should have been labeled as class 1, but in fact they are labeled as background. I wanted to ask if there is any approach for training using noisy labels?
  As an idea I wanted to use a teacher and student approach:
  
  1. start with a subset of clean labels
  2. train a model using the clean labels.
  3. generate pseudo-labels and filter out high quality labels
  4. combine them with previously clean data
  5. repeat the training process until a good performance is obtained.
  
  The main problem with this approach is that it is difficult to collect a subset of clean labels to start from. I do have lots of labeled data which may be useful during training although they are noisy.
  
  Please suggest any useful approach and resource here.
  
  **Tags:** computer-vision, image-segmentation, semi-supervised-learning
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-05-20. Score: 0
  
  One of the tools I like for a problem like this is a random forest. Because it uses both row (sample) and column (variable) bootstrapping, it tends to be robust and get around noise.
  
  If I had to attack this, I would use a random forest to start with, and confirm whether it was properly labeling outside the training set. For images that takes a bit of preprocessing. After it is up and working, I would distill it into something like a neural network that is more engineered to work with image data.
  
  Some clarifying questions:
  
  * What can you tell us about rates of false labeling? Is it a few percent per class, or a sizeable chunk?
  * Are you constrained to a particular architecture, platform, or toolset?
  * What more can you tell us about input and outputs? Sizes? Quantities? etc.
  
  
  ---
  
  