Abstract
Generative adversarial networks (GANs) suffer from catastrophic forgetting when learning multiple consecutive tasks. Parameter regularization methods that constrain the parameters of the new model in order to be close to the previous model through parameter importance are effective in overcoming forgetting. Many parameter regularization methods have been tried, but each of them is only suitable for limited types of neural networks. Aimed at GANs, this paper proposes a unified framework called Memory Protection GAN (MPGAN), in which many parametrization methods can be used to overcome forgetting. The proposed framework includes two modules: Protecting Weights in Generator and Controller. In order to incorporate parameter regularization methods into MPGAN, the Protecting Weights in Generator module encapsulates different parameter regularization methods into a “container”, and consolidates the most important parameters in the generator through a parameter regularization method selected from the container. In order to differentiate tasks, the Controller module creates unique tags for the tasks. Another problem with existing parameter regularization methods is their low accuracy in measuring parameter importance. These methods always rely on the first derivative of the output function, and ignore the second derivative. To assess parameter importance more accurately, a new parameter regularization method called Second Derivative Preserver (SDP), which takes advantage of the second derivative of the output function, is designed into MPGAN. Experiments demonstrate that MPGAN is applicable to multiple parameter regularization methods and that SDP achieves high accuracy in parameter importance.
Highlights
Generative adversarial networks (GANs) [1] have led to significant improvements in image generation [1], [2], image deblur [3], super-resolution [4], [5], and other domains
DETAILS OF EXPERIMENTS GAN models used in Memory Protection GAN (MPGAN): Three different GAN models were selected to demonstrate the effectiveness of MPGAN
The three GAN models were used to check the effectiveness of the MPGAN framework in overcoming catastrophic forgetting
Summary
Generative adversarial networks (GANs) [1] have led to significant improvements in image generation [1], [2], image deblur [3], super-resolution [4], [5], and other domains. When trained on multiple tasks arriving sequentially, GANs always forget previously learned knowledge, which is known as catastrophic forgetting [6]. Catastrophic forgetting severely restricts the applications for GANs. The associate editor coordinating the review of this manuscript and approving it for publication was Pengcheng Liu. in real-world scenarios [7]–[9]. When suffering from catastrophic forgetting, GANs would encounter the challenge of reusing learned knowledge from old patient data without storing them. Many methods have been proposed to alleviate catastrophic forgetting. The mainstream methods for overcoming forgetting of convolutional neural networks (CNNs) [8]–[19] can generally be divided into three categories: transfer learning approaches, rehearsal mechanisms, and
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