论文标题
分层量化自动编码器
Hierarchical Quantized Autoencoders
论文作者
论文摘要
尽管在训练神经网络方面取得了进展，以进行有损图像压缩，但当前的方法仍无法在非常低的比特率下保持感知质量和抽象特征。在最近通过量化量化变异自动编码器（VQ-VAE）学习离散表示的成功鼓励下，我们激励使用VQ-VAE的层次结构来达到高压缩因素。我们表明，随机量化和分层潜在结构的组合有助于基于可能性的图像压缩。这导致我们引入了一个新的培训层次结构VQ-VAE的目标。我们最终的方案产生了马尔可夫的一系列潜在变量，这些变量重建了高感知质量的图像，这些图像保留了语义上有意义的特征。我们在Celeba和MNIST数据集上提供定性和定量评估。
Despite progress in training neural networks for lossy image compression, current approaches fail to maintain both perceptual quality and abstract features at very low bitrates. Encouraged by recent success in learning discrete representations with Vector Quantized Variational Autoencoders (VQ-VAEs), we motivate the use of a hierarchy of VQ-VAEs to attain high factors of compression. We show that the combination of stochastic quantization and hierarchical latent structure aids likelihood-based image compression. This leads us to introduce a novel objective for training hierarchical VQ-VAEs. Our resulting scheme produces a Markovian series of latent variables that reconstruct images of high-perceptual quality which retain semantically meaningful features. We provide qualitative and quantitative evaluations on the CelebA and MNIST datasets.
