Traditional image clustering methods take a two-step approach, feature
learning and clustering, sequentially. However, recent research results
demonstrated that combining the separated phases in a unified framework and
training them jointly can achieve a better performance. In this paper, we first
introduce fully convolutional auto-encoders for image feature learning and then
propose a unified clustering framework to learn image representations and
cluster centers jointly based on a fully convolutional auto-encoder and soft
$k$-means scores. At initial stages of the learning procedure, the
representations extracted from the auto-encoder may not be very discriminative
for latter clustering. We address this issue by adopting a boosted
discriminative distribution, where high score assignments are highlighted and
low score ones are de-emphasized. With the gradually boosted discrimination,
clustering assignment scores are discriminated and cluster purities are
enlarged. Experiments on several vision benchmark datasets show that our
methods can achieve a state-of-the-art performance.

引入全卷积自编码器的图像特征学习和基于全卷积自编码器和软 k 均值分数的统一聚类框架，通过采用增强式判别分布，逐步提高聚类准确度，实验结果表明，该方法在多个视觉基准数据集上达到了最先进的性能。