Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
About
The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem. Bits-Back with Asymmetric Numeral Systems (BB-ANS), recently proposed by Townsend et al. (2019), makes bits-back coding practically feasible for latent variable models with one latent layer, but it is inefficient for hierarchical latent variable models. In this paper we propose Bit-Swap, a new compression scheme that generalizes BB-ANS and achieves strictly better compression rates for hierarchical latent variable models with Markov chain structure. Through experiments we verify that Bit-Swap results in lossless compression rates that are empirically superior to existing techniques. Our implementation is available at https://github.com/fhkingma/bitswap.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Density Estimation | MNIST (test) | NLL (bits/dim)1.27 | 56 | |
| Lossless Compression | CIFAR10 (test) | Bits Per Dimension (BPD)3.78 | 30 | |
| Density Estimation | Fashion (test) | NLL (bits/dim)3.28 | 27 | |
| Lossless Compression | ImageNet32 (test) | BPD4.23 | 20 | |
| Lossless Compression | CIFAR10 | BPD3.82 | 20 | |
| Lossless Compression | SVHN (test) | BPD2.55 | 16 | |
| Lossless Compression | CelebA 32x32 (test) | BPD3.82 | 16 | |
| Image Compression | ImageNet-32 | BPD4.5 | 14 | |
| Image Compression | ImageNet 32x32 (test) | BPD4.5 | 13 | |
| Image Compression | CIFAR10 | BPD3.82 | 13 |