Model Zoo: A Growing "Brain" That Learns Continually (2106.03027v3)

Abstract: This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models. We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them. The generalization error on a particular task can improve when it is trained with synergistic tasks, but can also deteriorate when trained with competing tasks. This theory motivates our method named Model Zoo which, inspired from the boosting literature, grows an ensemble of small models, each of which is trained during one episode of continual learning. We demonstrate that Model Zoo obtains large gains in accuracy on a variety of continual learning benchmark problems. Code is available at https://github.com/grasp-lyrl/modelzoo_continual.

• The paper presents a continual learning method, called \name, that incrementally expands an ensemble to balance task interference and mitigate catastrophic forgetting.
• It applies boosting-inspired techniques to distribute learning capacity across models for effective handling of both related and dissimilar tasks.
• Empirical results on benchmarks like Split-MiniImagenet demonstrate up to a 30% accuracy improvement, highlighting the method's practical impact on continual learning performance.

Insights into "A Growing 'Brain' That Learns Continually"

The paper "A Growing 'Brain' That Learns Continually" proposes a novel approach to continual learning called \name, which addresses the challenges of task interference and catastrophic forgetting by incrementally expanding the model's capacity across multiple models. This method draws inspiration from boosting to create an ensemble of small models, each designed to handle distinct sets of tasks. The central thesis is that dividing and distributing the learner's capacity across these models can result in better synergy among tasks and improved accuracy.

Conceptual and Theoretical Foundations

Continual learning systems aim to assimilate new tasks while retaining knowledge from prior tasks. The paper proposes that the efficacy of a continual learner is contingent on the complementary nature of the tasks. When tasks are related, they can be learned concurrently using a shared representation, leading to enhanced generalization. Conversely, when tasks are dissimilar, this shared learning can degrade performance. To mitigate such adverse interactions, the paper introduces a novel theoretical framework that analyzes task relatedness through statistical learning theories. The core idea is to balance the trade-off between synergetic tasks that facilitate knowledge transfer and competing tasks that contend for the model's limited capacity.

Algorithmic Development: \name

The algorithmic solution, \name, is designed to optimize the synergy between tasks by dynamically expanding an ensemble of models. Each model in the ensemble is refined in successive learning episodes, where it is trained on a mix of the current and previously encountered tasks. This approach is reminiscent of AdaBoost, wherein the model selection is biased towards those tasks that previously demonstrated higher error, thereby optimizing for those tasks where substantial learning can still be achieved.

Empirical Validation and Results

Through comprehensive experiments on several continual learning benchmarks like CIFAR-100, Split-MiniImagenet, and variants of MNIST, \name demonstrates substantial gains in per-task accuracy, signifying robust forward and backward knowledge transfer. Notably, \name achieves a 30% improvement on the Split-MiniImagenet benchmark compared to existing methods, highlighting its proficiency in utilizing task-related data effectively. The experiments corroborate the theoretical claims, showcasing \name's efficacy in enhancing model performance by relieving capacity constraints and leveraging task similarities.

Implications for Continual Learning and AI

The broader implications of this research touch upon both theoretical and practical dimensions of artificial intelligence. Theoretically, the paper advances the understanding of task interactions within the context of continual learning, providing a robust framework to guide future research efforts. Practically, \name offers a scalable solution to the prevailing challenge of catastrophic forgetting in neural networks, paving the path for more resilient and adaptive AI systems.

Future Directions

Looking ahead, several intriguing avenues for development emerge from this research. One aspect is the exploration of more sophisticated task similarity metrics to further refine task selection processes during model training. Additionally, extending \name's architecture to unsupervised or semi-supervised learning scenarios presents a promising research frontier. Finally, integrating \name within distributed AI systems could enhance the ability of such systems to learn continuously from diverse data streams across varied domains.

In conclusion, the paper's approach to continual learning represents a meaningful step in advancing AI systems towards more human-like learning paradigms, where knowledge is incrementally accrued, retained, and transferred across a myriad of tasks and contexts.