A popular approach to learning encoders for lossy compression is to use
additive uniform noise during training as a differentiable approximation to
test-time quantization. We demonstrate that a uniform noise channel can also be
implemented at test time using universal quantization (Ziv, 1985). This allows
us to eliminate the mismatch between training and test phases while maintaining
a completely differentiable loss function. Implementing the uniform noise
channel is a special case of the more general problem of communicating a
sample, which we prove is computationally hard if we do not make assumptions
about its distribution. However, the uniform special case is efficient as well
as easy to implement and thus of great interest from a practical point of view.
Finally, we show that quantization can be obtained as a limiting case of a soft
quantizer applied to the uniform noise channel, bridging compression with and
without quantization.

通过实现一种统一的量化方式，消除训练和测试阶段的不匹配，使我们能够使用可完全区分的损失函数学习编码器。当遇到没有关于分布的假设时，这种方法的通用性可能会面临计算上的困难。最后，我们还展示了量化如何通过软量化实现，从而将压缩与无量化桥接起来。