Enhancing model prediction confidence on target data is an important
objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore
adversarial training on penultimate activations, i.e., input features of the
final linear classification layer. We show that this strategy is more efficient
and better correlated with the objective of boosting prediction confidence than
adversarial training on input images or intermediate features, as used in
previous works. Furthermore, with activation normalization commonly used in
domain adaptation to reduce domain gap, we derive two variants and
systematically analyze the effects of normalization on our adversarial
training. This is illustrated both in theory and through empirical analysis on
real adaptation tasks. Extensive experiments are conducted on popular UDA
benchmarks under both standard setting and source-data free setting. The
results validate that our method achieves the best scores against previous
arts. Code is available at this https URL

以对抗训练为基础，使用最后一层线性分类器的激活输入特征进行无监督领域自适应预测，结合激活归一化技术，提高模型预测能力。

基于次最终激活的对抗性训练的领域自适应

Domain Adaptation with Adversarial Training on Penultimate Activations

A popular method to reduce the training time of deep neural networks is to
normalize activations at each layer. Although various normalization schemes
have been proposed, they all follow a common theme: normalize across spatial
dimensions and discard the extracted statistics. In this paper, we propose an
alternative normalization method that noticeably departs from this convention
and normalizes exclusively across channels. We argue that the channel dimension
is naturally appealing as it allows us to extract the first and second moments
of features extracted at a particular image position. These moments capture
structural information about the input image and extracted features, which
opens a new avenue along which a network can benefit from feature
normalization: Instead of disregarding the normalization constants, we propose
to re-inject them into later layers to preserve or transfer structural
information in generative networks. Codes are available at
this https URL

本文提出了一种基于通道归一化的激活值归一化方法，通过提取特征在图像位置的一阶和二阶矩来捕获输入图像和抽取特征的结构信息，并将归一化常数重新注入后续层以保留或转移结构信息在生成网络中。