Guided Variational Autoencoder for Disentanglement Learning
Guided Variational Autoencoder for Disentanglement Learning
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DOI：
10.1109/cvpr42600.2020.00794
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Zheng Ding;Yifan Xu;Weijian Xu;Gaurav Parmar;Yang Yang-Yang;M. Welling;Z. Tu
中科院分区：
文献类型：
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作者：
Zheng Ding;Yifan Xu;Weijian Xu;Gaurav Parmar;Yang Yang-Yang;M. Welling;Z. Tu
我们提出了一种算法，引导变分自动编码器（Guided-VAE），能够通过执行潜在表示解纠缠学习来学习可控的生成模型。学习的目标是通过提供信号的潜在的编码/嵌入在VAE不改变其主要骨干架构，从而保持所需的属性的VAE。我们在Guided-VAE中设计了一个无监督和有监督的策略，并观察到增强的建模和控制能力。在无监督策略中，我们通过引入学习潜在几何变换和主成分的轻量级解码器来指导VAE学习;在有监督策略中，我们使用对抗性激励和抑制机制来鼓励潜在变量的解开。Guided-VAE对于一般的表示学习任务以及解纠缠学习具有透明性和简单性。在一些表示学习的实验中，已经观察到改进的合成/采样，更好的分类解缠，以及减少Meta学习中的分类错误。
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signal to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed.