IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression - van den Berg, Rianne; A. Gritsenko, Alexey; Dehghani, Mostafa; Kaae Sønderby, Casper; Salimans, Tim (2021 International Conference on Learning Representations, 2021)
In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Their discrete nature makes them particularly suitable for lossless compression with entropy coding schemes. We start by investigating a recent theoretical claim that states that invertible flows for discrete random variables are less flexible than their continuous counterparts. We refute this claim with a proof for integer discrete flows. Furthermore, we zoom in on the effect of gradient bias due to the straight-through estimator in integer discrete flows, and demonstrate that its influence is highly dependent on architecture choices and less prominent than previously thought. Finally, we show how different modifications to the architecture improve the performance of this model class for lossless compression.