Learning invariant representations from images is one of the hardest
challenges facing computer vision. Spatial pooling is widely used to create
invariance to spatial shifting, but it is restricted to convolutional models.
In this paper, we propose a novel pooling method that can learn soft clustering
of features from image sequences. It is trained to improve the temporal
coherence of features, while keeping the information loss at minimum. Our
method does not use spatial information, so it can be used with
non-convolutional models too. Experiments on images extracted from natural
videos showed that our method can cluster similar features together. When
trained by convolutional features, auto-pooling outperformed traditional
spatial pooling on an image classification task, even though it does not use
the spatial topology of features.

该论文提出了一种无需使用空间信息的新型池化方法，可以学习图像序列的特征软聚类，从而改进特征的时间相干性，且在图像分类任务上表现优异。

Auto-pooling: 学习从图像序列中改善图像特征的不变性

Auto-pooling: Learning to Improve Invariance of Image Features from  Image Sequences

Learning invariant representations is an important problem in machine
learning and pattern recognition. In this paper, we present a novel framework
of transformation-invariant feature learning by incorporating linear
transformations into the feature learning algorithms. For example, we present
the transformation-invariant restricted Boltzmann machine that compactly
represents data by its weights and their transformations, which achieves
invariance of the feature representation via probabilistic max pooling. In
addition, we show that our transformation-invariant feature learning framework
can also be extended to other unsupervised learning methods, such as
autoencoders or sparse coding. We evaluate our method on several image
classification benchmark datasets, such as MNIST variations, CIFAR-10, and
STL-10, and show competitive or superior classification performance when
compared to the state-of-the-art. Furthermore, our method achieves
state-of-the-art performance on phone classification tasks with the TIMIT
dataset, which demonstrates wide applicability of our proposed algorithms to
other domains.

本文提出了一个新颖的具有转换不变性的特征学习框架，将线性转换纳入特征学习算法中，可应用于无监督学习方法，如自动编码器或稀疏编码，证明在 MNIST 变化，CIFAR-10 和 STL-10 等图像分类基准数据集上具有优越的分类性能并在 TIMIT 数据集上实现了最先进的电话分类任务的结果。

使用局部变换学习不变表示

Learning Invariant Representations with Local Transformations

We introduce a new neural architecture and an unsupervised algorithm for
learning invariant representations from temporal sequence of images. The system
uses two groups of complex cells whose outputs are combined multiplicatively:
one that represents the content of the image, constrained to be constant over
several consecutive frames, and one that represents the precise location of
features, which is allowed to vary over time but constrained to be sparse. The
architecture uses an encoder to extract features, and a decoder to reconstruct
the input from the features. The method was applied to patches extracted from
consecutive movie frames and produces orientation and frequency selective units
analogous to the complex cells in V1. An extension of the method is proposed to
train a network composed of units with local receptive field spread over a
large image of arbitrary size. A layer of complex cells, subject to sparsity
constraints, pool feature units over overlapping local neighborhoods, which
causes the feature units to organize themselves into pinwheel patterns of
orientation-selective receptive fields, similar to those observed in the
mammalian visual cortex. A feed-forward encoder efficiently computes the
feature representation of full images.

介绍一个新的神经网络架构和无监督算法，用于学习从时间序列图像中获得不变表示，该系统使用两组复杂细胞，一个表示图像的内容，另一个表示精确的位置特征，并且使用一个编码器提取特征。