Backward-Compatible Prediction Updates: A Probabilistic Approach

Understand

When machine learning systems meet real world applications, accuracy is only one of several requirements.

• In this paper, we assay a complementary perspective originating from the increasing availability of pre-trained and regularly improving state-of-the-art models.
• While new improved models develop at a fast pace, downstream tasks vary more slowly or stay constant.
• Assume that we have a large unlabelled data set for which we want to maintain accurate predictions.