论文标题
解开自动编码器（DAE）
Disentangling Autoencoders (DAE)
论文作者
论文摘要
根据组理论中对称性转换的原理，我们注意到潜在空间的分解（或解散）潜在空间的重要性。据我们所知，这是第一个确定性模型，旨在基于没有正规化器的自动编码器实现分离。将所提出的模型与基于自动编码器的七个最先进的生成模型进行了比较，并基于五个监督的分解指标进行了评估。实验结果表明，当每个特征的差异都不同时，提出的模型可以更好地分解。我们认为，这种模型会导致一个新的领域，以基于没有正规化器的自动编码器来进行分解学习。
Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, this is the first deterministic model that is aiming to achieve disentanglement based on autoencoders without regularizers. The proposed model is compared to seven state-of-the-art generative models based on autoencoders and evaluated based on five supervised disentanglement metrics. The experimental results show that the proposed model can have better disentanglement when variances of each features are different. We believe that this model leads to a new field for disentanglement learning based on autoencoders without regularizers.
