Deep Learning Meets Game Theory: Bregman-Based Algorithms for Interactive Deep Generative Adversarial Networks

Area of Science:

• Artificial Intelligence
• Machine Learning
• Game Theory

Background:

• Deep learning tasks can be framed as strategic games.
• Distributionally robust games are related to deep generative adversarial networks (GANs).

Purpose of the Study:

• To model deep learning tasks as strategic games.
• To introduce a speed-up deep learning algorithm using Bregman discrepancy for enhanced convergence rates.
• To analyze the performance of the proposed algorithm in deep generative adversarial networks (GANs).

Main Methods:

• Modeling deep learning tasks as strategic games with continuous action spaces.
• Utilizing distributionally robust games and their connection to GANs.
• Employing Bregman discrepancy to construct a speed-up deep learning method, avoiding second derivatives.
• Deriving the convergence rate of the proposed algorithm using a mean estimate.

Main Results:

• The Bregman deep learning algorithm achieves a higher-order convergence rate.
• Experiments on real datasets demonstrate the effectiveness of the algorithm in both shallow and deep GANs.
• Qualitative and quantitative results confirm that the generative model trained by the Bregman deep learning algorithm accelerates state-of-the-art performance.

Conclusions:

• The interplay between deep learning and game theory offers novel algorithmic approaches.
• Bregman deep learning provides an efficient method for training generative models.
• The proposed algorithm significantly speeds up performance in deep generative adversarial networks (GANs).