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Principled Knowledge Extrapolation with GANs
Ruili Feng · Jie Xiao · Kecheng Zheng · Deli Zhao · Jingren Zhou · Qibin Sun · Zheng-Jun Zha

Wed Jul 20 03:30 PM -- 05:30 PM (PDT) @ Hall E #207

Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs) into architectures of generator networks. This methodology, however, limits the flexibility of the generator as they must be carefully crafted to follow the causal graph, and demands a ground truth SCM with strong ignorability assumption as prior, which is a nontrivial assumption in many real scenarios. Thus, many current causal GAN methods fail to generate high fidelity counterfactual results as they cannot easily leverage state-of-the-art generative models. In this paper, we propose to study counterfactual synthesis from a new perspective of knowledge extrapolation, where a given knowledge dimension of the data distribution is extrapolated, but the remaining knowledge is kept indistinguishable from the original distribution. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem, and a novel principal knowledge descent method can efficiently estimate the extrapolated distribution through the adversarial game. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.

Author Information

Ruili Feng (USTC)
Jie Xiao (University of Science and Technology of China)
Kecheng Zheng (University of Science and Technology of China)
Deli Zhao (Alibaba Group)
Jingren Zhou (Alibaba Group)
Qibin Sun (University of Science and Technology of China)
Zheng-Jun Zha (University of Science and Technology of China)

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