Estimation with Uncertainty via Conditional Generative Adversarial Networks

• URL: http://arxiv.org/abs/2007.00334v1
• Date: Wed, 1 Jul 2020 08:54:17 GMT
• Title: Estimation with Uncertainty via Conditional Generative Adversarial Networks
• Authors: Minhyeok Lee, Junhee Seok
• Abstract summary: We propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in conditional Generative Adversarial Network (cGAN) By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems.
• Score: 3.829070379776576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management, in which discovering not only the prediction but also the uncertainty of the prediction is essentially required. To address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in conditional Generative Adversarial Network (cGAN) that has been routinely used for conditional sample generation. By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model; besides, the model is robust against noises since adversarial training is employed. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems, respectively. The proposed framework is applied to stock market data and an image classification task. As a result, the proposed framework shows superior estimation performance, especially on noisy data; moreover, it is demonstrated that the proposed framework can properly estimate the uncertainty of predictions.

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