Rate Distortion For Model Compression: From Theory To Practice
This work addresses model compression for efficient deployment in resource-limited settings, offering a novel theoretical foundation but with incremental practical improvements.
The paper tackles the problem of compressing deep neural networks for deployment in memory-constrained scenarios by proposing a principled framework based on rate distortion theory, which theoretically proves optimality for one-hidden-layer ReLU networks and empirically improves the compression-accuracy tradeoff over baseline methods.
The enormous size of modern deep neural networks makes it challenging to deploy those models in memory and communication limited scenarios. Thus, compressing a trained model without a significant loss in performance has become an increasingly important task. Tremendous advances has been made recently, where the main technical building blocks are parameter pruning, parameter sharing (quantization), and low-rank factorization. In this paper, we propose principled approaches to improve upon the common heuristics used in those building blocks, namely pruning and quantization. We first study the fundamental limit for model compression via the rate distortion theory. We bring the rate distortion function from data compression to model compression to quantify this fundamental limit. We prove a lower bound for the rate distortion function and prove its achievability for linear models. Although this achievable compression scheme is intractable in practice, this analysis motivates a novel model compression framework. This framework provides a new objective function in model compression, which can be applied together with other classes of model compressor such as pruning or quantization. Theoretically, we prove that the proposed scheme is optimal for compressing one-hidden-layer ReLU neural networks. Empirically, we show that the proposed scheme improves upon the baseline in the compression-accuracy tradeoff.