philippmwirth

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.