An obvious trick to speed up supervised learning is to label and import into the training set only the images for which the model makes wrong predictions. So for most of the images the human only needs to approve the automatic predictions - and from time to time he needs to label them.
Are there any libraries to facilitate a workflow like this?
We are currently working to support exactly this workflow with Lightly. The biggest challenge is to quickly and reliably find the images with wrong predictions. To tackle this, Lightly can leverage the strong representations from contrastive learning.
For example: A simple workflow for a classification task would be to train a self-supervised model on the whole dataset and find samples with a different annotation than their nearest neighbors. These can be identified quickly either in a colored scatter plot or by simply measuring disagreement.
All the active learning features and interaction with the platform already works with different frameworks such as Keras, Tensorflow or Jax.
The part for training self-supervised learning is currently only available for PyTorch. We don't have focused yet on bringing it to Tensorflow. But it's definitely something we should look at!
Are there any libraries to facilitate a workflow like this?