论文标题
评估深层生成模型的损耗压缩率
Evaluating Lossy Compression Rates of Deep Generative Models
论文作者
论文摘要
深层生成建模的领域已经成功地产生了令人惊讶的现实化图像和音频，但是定量评估仍然是一个挑战。 logikelihoody是一个吸引人的指标，由于其在统计和信息理论中的基础，但估算隐含生成模型的估算可能是具有挑战性的，而标量值指标给出了模型质量的不完整情况。在这项工作中，我们建议使用速率失真（RD）曲线来评估和比较深层生成模型。虽然估算RD曲线似乎比对数似然估计的计算要求更高，但我们表明，我们可以使用与以前用于实现单个对数似然估计的几乎相同的计算来近似整个RD曲线。我们评估了MNIST和CIFAR10数据集上VAE，GAN和对抗性自动编码器（AAE）的有损压缩率。测量整个RD曲线比标量值值指标可提供更完整的图像，并且我们获得了许多仅凭日志样式而言无法获得的见解。
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge. Log-likelihood is an appealing metric due to its grounding in statistics and information theory, but it can be challenging to estimate for implicit generative models, and scalar-valued metrics give an incomplete picture of a model's quality. In this work, we propose to use rate distortion (RD) curves to evaluate and compare deep generative models. While estimating RD curves is seemingly even more computationally demanding than log-likelihood estimation, we show that we can approximate the entire RD curve using nearly the same computations as were previously used to achieve a single log-likelihood estimate. We evaluate lossy compression rates of VAEs, GANs, and adversarial autoencoders (AAEs) on the MNIST and CIFAR10 datasets. Measuring the entire RD curve gives a more complete picture than scalar-valued metrics, and we arrive at a number of insights not obtainable from log-likelihoods alone.
