Continual Learning in Generative Adversarial Nets (1705.08395v1)

Abstract: Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.

• The paper introduces a modified EWC strategy that penalizes crucial network parameters to mitigate catastrophic forgetting in GANs.
• Experimental results on MNIST and SVHN validate that the approach maintains generative quality across sequential data without increasing model complexity.
• The method paves the way for applying GANs in real-world scenarios with continuously updated data, enhancing privacy-preserving and autonomous systems.

Continual Learning in Generative Adversarial Nets: A Technical Overview

This paper presents a methodological advancement for addressing the catastrophic forgetting in Generative Adversarial Networks (GANs) when tasked with continual learning across sequentially observed datasets. Specifically, the authors propose a scalable approach utilizing Elastic Weight Consolidation (EWC) to mitigate the degradation in GAN performance when previously encountered distributions need to be modeled concurrently with new datasets.

Problem Context and Solution

Generative Adversarial Networks, noted for their ability to generate realistic samples by modeling complex data distributions, often assume data is drawn independently and identically distributed (i.i.d.). This assumption falters in real-world scenarios where data arrives in sequences—such as incremental updates to datasets—and necessitates handling multiple distributions without collapsing performance. The intrinsic challenge posed by catastrophic forgetting in neural networks is well-documented; however, existing solutions predominantly target discriminative models via architectural adjustments or functional output preservation. This paper extends the focus to GANs, leveraging synaptic plasticity regulation to solve forgetting in generative scenarios.

Methodological Contributions

The authors adapt EWC, a strategy effective in discriminative neural networks, for GANs. EWC works by penalizing changes to network parameters deemed critical to the performance of previously learned tasks, as quantified by the Fisher Information Matrix. By modifying $G$'s objective function during sequential training, GANs learn to balance memory with the intake of new data distributions—thus preventing the erosion of performance on previous tasks. Specifically, they integrate conditional GAN frameworks where task-specific conditional inputs facilitate isolating learned distributions, obviating the standard need to regenerate old datasets for safeguarding past learning.

Experimental Validations

Evaluation on MNIST with simple MLP GANs and on SVHN with DCGANS shows promising results. The methodology preserved generative quality across multiple training epochs, demonstrating robustness against catastrophic forgetting without needing expanded model parameters. Notably, invariance was observed concerning the hyperparameter $\lambda$, suggesting a broad applicability range. The preservation of learned distributions against new classes, even when prior discriminators are inaccessible, further demonstrates the effectiveness of this approach.

Implications and Future Directions

This research represents a methodical advancement in enhancing the utility of GANs beyond static datasets. The ability to model sequentially incoming data paves the way for applying GANs to environments where real-time data updates are inevitable. The implications extend to fields requiring efficient data intake without extensive storage—such as privacy-preserving federated learning and dynamic autonomous systems. Future research trajectories could explore integrations with experience replay mechanisms or dynamically adaptable penalization regimes that tailor to data complexity and volume.

In summary, this paper lays essential groundwork for deploying GANs in real-world applications where data is unavoidably sequential. While the computational foundations are well-validated, ongoing exploration into adaptive hyperparameter tuning and implicated privacy constraints will further enrich practical applicability across AI domains.