We propose a GAN-based image compression method working at extremely low
bitrates below 0.1bpp. Most existing learned image compression methods suffer
from blur at extremely low bitrates. Although GAN can help to reconstruct sharp
images, there are two drawbacks. First, GAN makes training unstable. Second,
the reconstructions often contain unpleasing noise or artifacts. To address
both of the drawbacks, our method adopts two-stage training and network
interpolation. The two-stage training is effective to stabilize the training.
Moreover, the network interpolation utilizes the models in both stages and
reduces undesirable noise and artifacts, while maintaining important edges.
Hence, we can control the trade-off between perceptual quality and fidelity
without re-training models. The experimental results show that our model can
reconstruct high quality images. Furthermore, our user study confirms that our
reconstructions are preferable to state-of-the-art GAN-based image compression
model. The code will be available.

一种基于 GAN 的图像压缩方法，通过采用两阶段的训练和网络插值技术，在极低比特率下重构高质量图像，可以平衡感知质量和保真度之间的权衡且优于现有的基于 GAN 的图像压缩模型。

具备保真度可控的极端图像压缩技术：生成式对抗网络

Fidelity-Controllable Extreme Image Compression with Generative  Adversarial Networks

Deep convolutional neural network has demonstrated its capability of learning
a deterministic mapping for the desired imagery effect. However, the large
variety of user flavors motivates the possibility of continuous transition
among different output effects. Unlike existing methods that require a specific
design to achieve one particular transition (e.g., style transfer), we propose
a simple yet universal approach to attain a smooth control of diverse imagery
effects in many low-level vision tasks, including image restoration,
image-to-image translation, and style transfer. Specifically, our method,
namely Deep Network Interpolation (DNI), applies linear interpolation in the
parameter space of two or more correlated networks. A smooth control of imagery
effects can be achieved by tweaking the interpolation coefficients. In addition
to DNI and its broad applications, we also investigate the mechanism of network
interpolation from the perspective of learned filters.

本文提出了深度网络插值（DNI）的方法，应用于多个低级图像任务，包括图像恢复、图像到图像的转换和样式迁移，达到了多样图像效果的平滑控制，并从学习过滤器的角度探究了网络插值机制。