Neural compression algorithms are typically based on autoencoders that
require specialized encoder and decoder architectures for different data
modalities. In this paper, we propose COIN++, a neural compression framework
that seamlessly handles a wide range of data modalities. Our approach is based
on converting data to implicit neural representations, i.e. neural functions
that map coordinates (such as pixel locations) to features (such as RGB
values). Then, instead of storing the weights of the implicit neural
representation directly, we store modulations applied to a meta-learned base
network as a compressed code for the data. We further quantize and entropy code
these modulations, leading to large compression gains while reducing encoding
time by two orders of magnitude compared to baselines. We empirically
demonstrate the feasibility of our method by compressing various data
modalities, from images and audio to medical and climate data.

COIN++ 提供了一种神经压缩框架，它基于将数据转换为隐式神经表示，并利用元学习的基网络来存储应用于数据的调制，从而实现大量压缩收益并降低编码时间。

COIN++：跨模态神经压缩

COIN++: Neural Compression Across Modalities

The present paper develops a novel aggregated gradient approach for
distributed machine learning that adaptively compresses the gradient
communication. The key idea is to first quantize the computed gradients, and
then skip less informative quantized gradient communications by reusing
outdated gradients. Quantizing and skipping result in `lazy' worker-server
communications, which justifies the term Lazily Aggregated Quantized gradient
that is henceforth abbreviated as LAQ. Our LAQ can provably attain the same
linear convergence rate as the gradient descent in the strongly convex case,
while effecting major savings in the communication overhead both in transmitted
bits as well as in communication rounds. Empirically, experiments with real
data corroborate a significant communication reduction compared to existing
gradient- and stochastic gradient-based algorithms.

本文提出了一种新的集成梯度方法来进行分布式机器学习，其通过量化计算的梯度避免信息不足的梯度通信并高效减少通信开销。实验证明，与现有的基于梯度或随机梯度的算法相比，该算法可以显著减少通信量和通信轮数。