Disentangling Factors of Variation via Generative Entangling
Disentangling Factors of Variation via Generative Entangling
复制标题
通过生成纠缠解开变异因素
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Yoshua Bengio
中科院分区：
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作者：
Guillaume Desjardins;Aaron C. Courville;Yoshua Bengio
在这里，我们提出了一个新的模型家庭的学习，以解开数据的变化因素的目标。我们的方法是基于穗板限制玻尔兹曼机，我们概括为包括多个潜变量之间的高阶相互作用。从生成的角度来看，乘法互动模仿变异因素的纠缠。模型中的推理可以被看作是解开这些生成因素。与以前试图解开潜在因素不同，所提出的模型是使用没有监督的信息来训练的。我们将我们的模型应用于面部表情分类的任务。
Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the multiplicative interactions emulates the entangling of factors of variation. Inference in the model can be seen as disentangling these generative factors. Unlike previous attempts at disentangling latent factors, the proposed model is trained using no supervised information regarding the latent factors. We apply our model to the task of facial expression classification.