Quantization has been applied to multiple domains in Deep Neural Networks
(DNNs). We propose Depthwise Quantization (DQ) where $\textit{quantization}$ is
applied to a decomposed sub-tensor along the $\textit{feature axis}$ of weak
statistical dependence. The feature decomposition leads to an exponential
increase in $\textit{representation capacity}$ with a linear increase in memory
and parameter cost. In addition, DQ can be directly applied to existing
encoder-decoder frameworks without modification of the DNN architecture. We use
DQ in the context of Hierarchical Auto-Encoder and train end-to-end on an image
feature representation. We provide an analysis on cross-correlation between
spatial and channel features and we propose a decomposition of the image
feature representation along the channel axis. The improved performance of the
depthwise operator is due to the increased representation capacity from
implicit feature decoupling. We evaluate DQ on the likelihood estimation task,
where it outperforms the previous state-of-the-art on CIFAR-10, ImageNet-32 and
ImageNet-64. We progressively train with increasing image size a single
hierarchical model that uses 69% less parameters and has a faster convergence
than the previous works.

通过使用 Depthwise Quantization 方法对特征轴上的分解子张量进行量化，增加了表现能力，同时在现有编码器 - 解码器框架中可以直接应用，提高了对 CIFAR-10、ImageNet-32 和 ImageNet-64 的似然度估计能力。

深度量化的隐性特征解耦

Implicit Feature Decoupling with Depthwise Quantization

Unsupervised domain adaptive classifcation intends to improve the
classifcation performance on unlabeled target domain. To alleviate the adverse
effect of domain shift, many approaches align the source and target domains in
the feature space. However, a feature is usually taken as a whole for alignment
without explicitly making domain alignment proactively serve the classifcation
task, leading to sub-optimal solution. In this paper, we propose an effective
Task-oriented Alignment (ToAlign) for unsupervised domain adaptation (UDA). We
study what features should be aligned across domains and propose to make the
domain alignment proactively serve classifcation by performing feature
decomposition and alignment under the guidance of the prior knowledge induced
from the classifcation task itself. Particularly, we explicitly decompose a
feature in the source domain into a task-related/discriminative feature that
should be aligned, and a task-irrelevant feature that should be
avoided/ignored, based on the classifcation meta-knowledge. Extensive
experimental results on various benchmarks (e.g., Offce-Home, Visda-2017, and
DomainNet) under different domain adaptation settings demonstrate the
effectiveness of ToAlign which helps achieve the state-of-the-art performance.
The code is publicly available at this https URL

本文提出了一种名为 ToAlign 的有效的无监督领域适应方法，该方法通过性能指导的特征分解和对齐，在源域和目标域之间实现了任务导向的对齐来实现最优解。