论文标题
与势能排名的域去相关
Domain Decorrelation with Potential Energy Ranking
论文作者
论文摘要
机器学习系统，尤其是基于深度学习的方法，在实验设置下的现代计算机视觉任务中取得了巨大成功。通常，这些经典的深度学习方法建立在\ emph {i.i.d。}假设上，假设训练和测试数据是从独立且相同的相似分布中绘制的。但是，上述\ emph {i.i.d。}的假设通常在现实情况下不可用，因此导致深度学习算法的急剧性能衰减。在此背后，域转移是要责备的主要因素之一。 In order to tackle this problem, we propose using \textbf{Po}tential \textbf{E}nergy \textbf{R}anking (PoER) to decouple the object feature and the domain feature (\emph{i.e.,} appearance feature) in given images, promoting the learning of label-discriminative features while filtering out the irrelevant correlations between the objects and the 背景。 POER可帮助神经网络捕获与标签相关的特征，这些特征首先包含域信息，然后逐渐逐步提取标签 - 歧义表示形式，从而强制执行神经网络以意识到对象和背景的特征，这对于生成域Invariant特征至关重要。 Poer报告了域泛化基准的出色性能，与现有方法相比，平均TOP-1的准确性至少提高了1.20 \％。此外，我们在ECCV 2022 NICO Challenge \ footNote {https://nicochallenge.com}中使用POER，仅使用Vanilla Resnet-18获得顶级。该代码已在https://github.com/foreverps/poer上提供。
Machine learning systems, especially the methods based on deep learning, enjoy great success in modern computer vision tasks under experimental settings. Generally, these classic deep learning methods are built on the \emph{i.i.d.} assumption, supposing the training and test data are drawn from a similar distribution independently and identically. However, the aforementioned \emph{i.i.d.} assumption is in general unavailable in the real-world scenario, and as a result, leads to sharp performance decay of deep learning algorithms. Behind this, domain shift is one of the primary factors to be blamed. In order to tackle this problem, we propose using \textbf{Po}tential \textbf{E}nergy \textbf{R}anking (PoER) to decouple the object feature and the domain feature (\emph{i.e.,} appearance feature) in given images, promoting the learning of label-discriminative features while filtering out the irrelevant correlations between the objects and the background. PoER helps the neural networks to capture label-related features which contain the domain information first in shallow layers and then distills the label-discriminative representations out progressively, enforcing the neural networks to be aware of the characteristic of objects and background which is vital to the generation of domain-invariant features. PoER reports superior performance on domain generalization benchmarks, improving the average top-1 accuracy by at least 1.20\% compared to the existing methods. Moreover, we use PoER in the ECCV 2022 NICO Challenge\footnote{https://nicochallenge.com}, achieving top place with only a vanilla ResNet-18. The code has been made available at https://github.com/ForeverPs/PoER.
