Although CNNs are believed to be invariant to translations, recent works have
shown this is not the case, due to aliasing effects that stem from downsampling
layers. The existing architectural solutions to prevent aliasing are partial
since they do not solve these effects, that originate in non-linearities. We
propose an extended anti-aliasing method that tackles both downsampling and
non-linear layers, thus creating truly alias-free, shift-invariant CNNs. We
show that the presented model is invariant to integer as well as fractional
(i.e., sub-pixel) translations, thus outperforming other shift-invariant
methods in terms of robustness to adversarial translations.

本文提出一种扩展的反锯齿方法，旨在解决由下采样层和非线性层引起的失真问题，从而创造真正无失真的平移不变的 CNN，并且在整数和分数（即次像素）平移方面具有不变性，因此在对抗平移的鲁棒性方面优于其他平移不变方法。

无别名卷积网络：通过多项式激活实现分数移不变性

Alias-Free Convnets: Fractional Shift Invariance via Polynomial  Activations

We propose learnable polyphase sampling (LPS), a pair of learnable
down/upsampling layers that enable truly shift-invariant and equivariant
convolutional networks. LPS can be trained end-to-end from data and generalizes
existing handcrafted downsampling layers. It is widely applicable as it can be
integrated into any convolutional network by replacing down/upsampling layers.
We evaluate LPS on image classification and semantic segmentation. Experiments
show that LPS is on-par with or outperforms existing methods in both
performance and shift consistency. For the first time, we achieve true
shift-equivariance on semantic segmentation (PASCAL VOC), i.e., 100% shift
consistency, outperforming baselines by an absolute 3.3%.

本文提出了可学习的多相采样（LPS），它是一对可学习的下 / 上采样层，使得卷积网络能够真正具有平移不变性和等变性，可以替换任何卷积网络的下 / 上采样层，本文在图像分类和语义分割方面对 LPS 进行了评估，实验表明，LPS 在性能和平移一致性方面与现有方法相当或优于现有方法。本文首次在语义分割（PASCAL VOC）中实现了真正的平移等变性，即 100％的平移一致性，优于基线 3.3％。

可学习多相采样的平移不变和等变卷积网络

Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks

Pan-sharpening is a process of merging a high-resolution (HR) panchromatic
(PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image
to create an HR-MS and pan-sharpened image. However, due to the different
sensors' locations, characteristics and acquisition time, PAN and MS image
pairs often tend to have various amounts of misalignment. Conventional
deep-learning-based methods that were trained with such misaligned PAN-MS image
pairs suffer from diverse artifacts such as double-edge and blur artifacts in
the resultant PAN-sharpened images. In this paper, we propose a novel framework
called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net)
which is the first method to take into account such large misalignment of
moving object regions for PAN sharpening. The SISPA-Net has a feature alignment
module (FAM) that can adjust one feature to be aligned to another feature, even
between the two different PAN and MS domains. For better alignment in
pan-sharpened images, a shift-invariant spectral loss is newly designed, which
ignores the inherent misalignment in the original MS input, thereby having the
same effect as optimizing the spectral loss with a well-aligned MS image.
Extensive experimental results show that our SIPSA-Net can generate
pan-sharpened images with remarkable improvements in terms of visual quality
and alignment, compared to the state-of-the-art methods.

本文提出了一种新颖的移动物体对齐的平移不变全色增强框架 SIPSA-Net，该框架具有特征对齐模块和新设计的平移不变谱损失函数，实验结果表明，相对于现有技术，SIPSA-Net 在视觉质量和对齐方面均有很大的改进。