Domain Adversarial Training: A Game Perspective

• URL: http://arxiv.org/abs/2202.05352v1
• Date: Thu, 10 Feb 2022 22:17:30 GMT
• Title: Domain Adversarial Training: A Game Perspective
• Authors: David Acuna, Marc T Law, Guojun Zhang, Sanja Fidler
• Abstract summary: This paper defines optimal solutions in domain-adversarial training from a game theoretical perspective. We show that descent in domain-adversarial training can violate the convergence guarantees of the gradient, oftentimes hindering the transfer performance. Ours are easy to implement, free of additional parameters, and can be plugged into any domain-adversarial framework.
• Score: 80.3821370633883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a game theoretical perspective. Defining optimal solutions in domain-adversarial training as a local Nash equilibrium, we show that gradient descent in domain-adversarial training can violate the asymptotic convergence guarantees of the optimizer, oftentimes hindering the transfer performance. Our analysis leads us to replace gradient descent with high-order ODE solvers (i.e., Runge-Kutta), for which we derive asymptotic convergence guarantees. This family of optimizers is significantly more stable and allows more aggressive learning rates, leading to high performance gains when used as a drop-in replacement over standard optimizers. Our experiments show that in conjunction with state-of-the-art domain-adversarial methods, we achieve up to 3.5% improvement with less than of half training iterations. Our optimizers are easy to implement, free of additional parameters, and can be plugged into any domain-adversarial framework.

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