论文标题
改善神经图像压缩的推断
Improving Inference for Neural Image Compression
论文作者
论文摘要
我们考虑使用深层变量模型的有损图像压缩问题。最先进的方法建立在层次变化自动编码器（VAE）上，并学习推理网络，以预测每个数据点的可压缩潜在表示。利用压缩的变异推理角度，我们确定了三个近似差距，这些近似差距限制了传统方法中的性能：摊销差距，离散差距和边缘化差距。我们根据与迭代推理，随机退火进行离散优化和折叠式编码有关的这三个限制的每个局限性提出补救措施，从而首次应用了BITS-BACK编码为有损压缩。在我们的实验中，包括广泛的基线比较和消融研究，我们仅通过更改推理方法来实现使用已建立的VAE架构在有损图像压缩方面的最新性能。
We consider the problem of lossy image compression with deep latent variable models. State-of-the-art methods build on hierarchical variational autoencoders (VAEs) and learn inference networks to predict a compressible latent representation of each data point. Drawing on the variational inference perspective on compression, we identify three approximation gaps which limit performance in the conventional approach: an amortization gap, a discretization gap, and a marginalization gap. We propose remedies for each of these three limitations based on ideas related to iterative inference, stochastic annealing for discrete optimization, and bits-back coding, resulting in the first application of bits-back coding to lossy compression. In our experiments, which include extensive baseline comparisons and ablation studies, we achieve new state-of-the-art performance on lossy image compression using an established VAE architecture, by changing only the inference method.
