Thanks to the use of convolution and pooling layers, convolutional neural
networks were for a long time thought to be shift-invariant. However, recent
works have shown that the output of a CNN can change significantly with small
shifts in input: a problem caused by the presence of downsampling (stride)
layers. The existing solutions rely either on data augmentation or on
anti-aliasing, both of which have limitations and neither of which enables
perfect shift invariance. Additionally, the gains obtained from these methods
do not extend to image patterns not seen during training. To address these
challenges, we propose adaptive polyphase sampling (APS), a simple sub-sampling
scheme that allows convolutional neural networks to achieve 100% consistency in
classification performance under shifts, without any loss in accuracy. With
APS, the networks exhibit perfect consistency to shifts even before training,
making it the first approach that makes convolutional neural networks truly
shift-invariant.

本文提出了自适应多相采样（APS）的简单子采样方案，使卷积神经网络实现了 100% 的 “移位一致” 的分类性能，而且拥有训练前的完美 “移位一致性”，这是使卷积神经网络真正具有 “移位不变性” 的第一实践方法。

真正平移不变卷积神经网络

Truly shift-invariant convolutional neural networks

Modern convolutional networks are not shift-invariant, as small input shifts
or translations can cause drastic changes in the output. Commonly used
downsampling methods, such as max-pooling, strided-convolution, and
average-pooling, ignore the sampling theorem. The well-known signal processing
fix is anti-aliasing by low-pass filtering before downsampling. However, simply
inserting this module into deep networks degrades performance; as a result, it
is seldomly used today. We show that when integrated correctly, it is
compatible with existing architectural components, such as max-pooling and
strided-convolution. We observe \textit{increased accuracy} in ImageNet
classification, across several commonly-used architectures, such as ResNet,
DenseNet, and MobileNet, indicating effective regularization. Furthermore, we
observe \textit{better generalization}, in terms of stability and robustness to
input corruptions. Our results demonstrate that this classical signal
processing technique has been undeservingly overlooked in modern deep networks.
Code and anti-aliased versions of popular networks are available at
this https URL .

本篇研究提出，在现代卷积网络中添加抗锯齿模块能较好地提高模型的精度，使其具有更好的普适性和鲁棒性。

使卷积网络再次具有平移不变性

Making Convolutional Networks Shift-Invariant Again

Most state-of-the-art action feature extractors involve differential
operators, which act as highpass filters and tend to attenuate low frequency
action information. This attenuation introduces bias to the resulting features
and generates ill-conditioned feature matrices. The Gaussian Pyramid has been
used as a feature enhancing technique that encodes scale-invariant
characteristics into the feature space in an attempt to deal with this
attenuation. However, at the core of the Gaussian Pyramid is a convolutional
smoothing operation, which makes it incapable of generating new features at
coarse scales. In order to address this problem, we propose a novel feature
enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks
features extracted using a family of differential filters parameterized with
multiple time skips and encodes shift-invariance into the frequency space. MIFS
compensates for information lost from using differential operators by
recapturing information at coarse scales. This recaptured information allows us
to match actions at different speeds and ranges of motion. We prove that MIFS
enhances the learnability of differential-based features exponentially. The
resulting feature matrices from MIFS have much smaller conditional numbers and
variances than those from conventional methods. Experimental results show
significantly improved performance on challenging action recognition and event
detection tasks. Specifically, our method exceeds the state-of-the-arts on
Hollywood2, UCF101 and UCF50 datasets and is comparable to state-of-the-arts on
HMDB51 and Olympics Sports datasets. MIFS can also be used as a speedup
strategy for feature extraction with minimal or no accuracy cost.

提出一种名为 MIFS 的特征增强技术，通过堆叠家族差分滤波器产生的特征并将其编码为频率空间中的移位不变性，以从格外视角重新捕获信息来改善动作识别的性能