Estimation with Uncertainty via Conditional Generative Adversarial Networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Conventional Artificial Neural Networks (ANNs) provide deterministic point estimates, limiting their use in fields requiring prediction uncertainty.
• Applications in medical diagnosis, law, and finance necessitate understanding prediction confidence alongside the prediction itself.

Purpose of the Study:

• To propose a novel predictive probabilistic neural network model.
• To address the limitations of deterministic ANNs by incorporating uncertainty estimation.
• To enhance the robustness and applicability of neural networks in critical decision-making domains.

Main Methods:

• A predictive probabilistic neural network model is proposed, adapting conditional Generative Adversarial Networks (cGANs) by reversing input/output.
• Adversarial training is employed to enhance model robustness against noisy data.
• Entropy and relative entropy are introduced for uncertainty measurement in regression and classification tasks, respectively.

Main Results:

• The proposed framework demonstrates superior estimation performance, particularly on noisy datasets.
• The model effectively estimates the uncertainty associated with its predictions.
• Successful application demonstrated on stock market data and an image classification task.

Conclusions:

• The developed probabilistic neural network offers a robust alternative to conventional ANNs for tasks requiring uncertainty quantification.
• The framework provides a reliable method for estimating prediction uncertainty, enhancing decision-making in complex and noisy environments.
• This approach advances the practical application of neural networks in sensitive fields like finance and healthcare.