Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines (1810.12488v4)

Abstract: Continual learning has received a great deal of attention recently with several approaches being proposed. However, evaluations involve a diverse set of scenarios making meaningful comparison difficult. This work provides a systematic categorization of the scenarios and evaluates them within a consistent framework including strong baselines and state-of-the-art methods. The results provide an understanding of the relative difficulty of the scenarios and that simple baselines (Adagrad, L2 regularization, and naive rehearsal strategies) can surprisingly achieve similar performance to current mainstream methods. We conclude with several suggestions for creating harder evaluation scenarios and future research directions. The code is available at https://github.com/GT-RIPL/Continual-Learning-Benchmark

Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

The paper, authored by Yen-Chang Hsu et al., provides a comprehensive evaluation of continual learning scenarios, challenging the current evaluation methodologies and advocating for the use of strong baselines. Continual learning, essential for evolving intelligent systems, faces significant challenges due to issues like catastrophic interference in neural networks. This paper systematically categorizes existing experimental methodologies while critically assessing their efficacy.

Key Contributions

The authors introduce three primary contributions:

1. Categorization Framework: They present a robust categorization of existing continual learning scenarios. This categorization is based on the variations in task domains, class distributions, and task identity, facilitating better understanding and evaluation.
2. Uniform Evaluation Framework: A consistent framework for scenario generation and evaluation is proposed. This framework helps in comparing state-of-the-art methods against simple yet effective baseline methods such as Adagrad, L2 regularization, and naive rehearsal strategies.
3. Baseline Effectiveness: The paper demonstrates that simple baselines often achieve performance comparable to, or even surpassing, more complex state-of-the-art methods. This emphasizes the need to reconsider the perceived complexity of proposed methods in continual learning.

Experimental Results

The experimentation is structured around both the Split MNIST and Permuted MNIST datasets, which are used to generate various continual learning scenarios: incremental domain learning, incremental class learning, and incremental task learning. The results underscore several key points:

• Baseline Performance: Surprisingly, naive rehearsal and Adagrad exhibit strong performance across various scenarios, often outperforming more complex methods like EWC and SI. For instance, naive rehearsal strategies achieve near-optimal performance in incremental class learning scenarios.
• Scenario Difficulty: There is a clear delineation in difficulty; incremental class learning is notably more challenging than other evaluated scenarios, suggesting an area for future methodological improvements.
• Regularization-Based Limitations: The analysis points out the limitations of regularization-based methods, especially when task boundaries are unknown—posing questions about their applicability in real-world, seamless learning environments.

Implications and Future Directions

The implications of this research are twofold:

1. Practical Development: From a practical perspective, the findings suggest that employing well-tuned, simpler baselines can be more efficient than adopting complex, memory-intensive methods. This has significant implications for developing scalable and efficient continual learning systems in dynamic environments.
2. Theoretical Insights: Theoretically, the results challenge prevailing assumptions about the superiority of highly sophisticated methods, encouraging the community to revisit foundational elements such as optimization strategies and memory management.

Future research should focus on refining evaluation scenarios to more accurately reflect real-world applications, which often do not provide clear task demarcations. Moreover, exploring mechanisms that navigate scenarios without task identity will bridge the gap between current methodologies and practical applications.

In conclusion, this paper makes a compelling case for re-evaluating methodologies in continual learning, advocating for robust baselines, and highlighting the limitations of existing approaches. Its insights have the potential to guide future developments and nurture advancements in adaptive AI systems.