Recent advances in image clustering typically focus on learning better deep
representations. In contrast, we present an orthogonal approach that does not
rely on abstract features but instead learns to predict image transformations
and performs clustering directly in image space. This learning process
naturally fits in the gradient-based training of K-means and Gaussian mixture
model, without requiring any additional loss or hyper-parameters. It leads us
to two new deep transformation-invariant clustering frameworks, which jointly
learn prototypes and transformations. More specifically, we use deep learning
modules that enable us to resolve invariance to spatial, color and
morphological transformations. Our approach is conceptually simple and comes
with several advantages, including the possibility to easily adapt the desired
invariance to the task and a strong interpretability of both cluster centers
and assignments to clusters. We demonstrate that our novel approach yields
competitive and highly promising results on standard image clustering
benchmarks. Finally, we showcase its robustness and the advantages of its
improved interpretability by visualizing clustering results over real
photograph collections.

本研究基于深度学习，提出了新的图像聚类方法，通过学习图像变换并在图像空间直接进行聚类，并可以轻松处理聚类中的不变性，实现了对聚类中心和聚类分配的解释性。研究表明，该方法在标准图像聚类基准测试中具有极高的竞争性和前景性。

深度变换不变聚类

Deep Transformation-Invariant Clustering

In this paper we present a deep neural network topology that incorporates a
simple to implement transformation invariant pooling operator (TI-POOLING).
This operator is able to efficiently handle prior knowledge on nuisance
variations in the data, such as rotation or scale changes. Most current methods
usually make use of dataset augmentation to address this issue, but this
requires larger number of model parameters and more training data, and results
in significantly increased training time and larger chance of under- or
overfitting. The main reason for these drawbacks is that the learned model
needs to capture adequate features for all the possible transformations of the
input. On the other hand, we formulate features in convolutional neural
networks to be transformation-invariant. We achieve that using parallel siamese
architectures for the considered transformation set and applying the TI-POOLING
operator on their outputs before the fully-connected layers. We show that this
topology internally finds the most optimal "canonical" instance of the input
image for training and therefore limits the redundancy in learned features.
This more efficient use of training data results in better performance on
popular benchmark datasets with smaller number of parameters when comparing to
standard convolutional neural networks with dataset augmentation and to other
baselines.

本文介绍了一种深度神经网络拓扑结构，该结构包含一种容易实现的 TI-POOLING 变换不变池化算子，能够有效地处理数据中的先验干扰因素，如旋转或比例变化，通过采用并行的 siamese 结构提取转换不变的特征，再在全连接层之前应用 TI-POOLING 算子，能够更高效地利用训练数据，相比于普通卷积神经网络进行数据集扩充的方法以及其他基线模型，能够在流行的基准数据集上具有更好的性能。