Dual Adversarial Auto-Encoders for Clustering
- URL: http://arxiv.org/abs/2008.10038v1
- Date: Sun, 23 Aug 2020 13:16:34 GMT
- Title: Dual Adversarial Auto-Encoders for Clustering
- Authors: Pengfei Ge, Chuan-Xian Ren, Jiashi Feng, Shuicheng Yan
- Abstract summary: We propose Dual Adversarial Auto-encoder (Dual-AAE) for unsupervised clustering.
By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders.
Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods.
- Score: 152.84443014554745
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As a powerful approach for exploratory data analysis, unsupervised clustering
is a fundamental task in computer vision and pattern recognition. Many
clustering algorithms have been developed, but most of them perform
unsatisfactorily on the data with complex structures. Recently, Adversarial
Auto-Encoder (AAE) shows effectiveness on tackling such data by combining
Auto-Encoder (AE) and adversarial training, but it cannot effectively extract
classification information from the unlabeled data. In this work, we propose
Dual Adversarial Auto-encoder (Dual-AAE) which simultaneously maximizes the
likelihood function and mutual information between observed examples and a
subset of latent variables. By performing variational inference on the
objective function of Dual-AAE, we derive a new reconstruction loss which can
be optimized by training a pair of Auto-encoders. Moreover, to avoid mode
collapse, we introduce the clustering regularization term for the category
variable. Experiments on four benchmarks show that Dual-AAE achieves superior
performance over state-of-the-art clustering methods. Besides, by adding a
reject option, the clustering accuracy of Dual-AAE can reach that of supervised
CNN algorithms. Dual-AAE can also be used for disentangling style and content
of images without using supervised information.
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