Towards Reproducible Learning-based Compression
This addresses the issue of reproducibility for deploying learning-based compression in real-world applications, but it is incremental as it builds on existing compression methods.
The paper tackles the problem of irreproducibility in deep learning-based compression systems, where hardware or software differences can cause decoding failures, and proposes a safeguarding mechanism that ensures reproducibility by bounding mismatches, with experiments showing effectiveness in image and point cloud compression.
A deep learning system typically suffers from a lack of reproducibility that is partially rooted in hardware or software implementation details. The irreproducibility leads to skepticism in deep learning technologies and it can hinder them from being deployed in many applications. In this work, the irreproducibility issue is analyzed where deep learning is employed in compression systems while the encoding and decoding may be run on devices from different manufacturers. The decoding process can even crash due to a single bit difference, e.g., in a learning-based entropy coder. For a given deep learning-based module with limited resources for protection, we first suggest that reproducibility can only be assured when the mismatches are bounded. Then a safeguarding mechanism is proposed to tackle the challenges. The proposed method may be applied for different levels of protection either at the reconstruction level or at a selected decoding level. Furthermore, the overhead introduced for the protection can be scaled down accordingly when the error bound is being suppressed. Experiments demonstrate the effectiveness of the proposed approach for learning-based compression systems, e.g., in image compression and point cloud compression.