  Bloom filters are widely used data structures that compactly represent sets
of elements. Querying a Bloom filter reveals if an element is not included in
the underlying set or is included with a certain error rate. This membership
testing can be modeled as a binary classification problem and solved through
deep learning models, leading to what is called learned Bloom filters. We have
identified that the benefits of learned Bloom filters are apparent only when
considering a vast amount of data, and even then, there is a possibility to
further reduce their memory consumption. For that reason, we introduce a
lossless input compression technique that improves the memory consumption of
the learned model while preserving a comparable model accuracy. We evaluate our
approach and show significant memory consumption improvements over learned
Bloom filters.
