Online Learned Continual Compression with Adaptive Quantization Modules

Understand

We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a representative dataset from a non i.i.d data stream, while only observing each sample once.

• A naive application of auto-encoders in this setting encounters a major challenge: representations derived from earlier encoder states must be usable by later decoder states.
• We show how to use discrete auto-encoders to effectively address this challenge and introduce Adaptive Quantization Modules (AQM) to control variation in the compression ability of the module at any given stage of learning.
• This enables selecting an appropriate compression for incoming samples, while taking into account overall memory constraints and current progress of the learned compression.