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.

Any plans for a Tensorflow support?

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!