ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think

Abstract: Backpropagation provides a generalized configuration for overcoming catastrophic forgetting. Optimizers such as SGD and Adam are commonly used for weight updates in continual learning and continual pre-training. However, access to gradient information is not always feasible in practice due to black-box APIs, hardware constraints, or non-differentiable systems, a challenge we refer to as the gradient bans. To bridge this gap, we introduce ZeroFlow, the first benchmark designed to evaluate gradient-free optimization algorithms for overcoming forgetting. ZeroFlow examines a suite of forward pass-based methods across various algorithms, forgetting scenarios, and datasets. Our results show that forward passes alone can be sufficient to mitigate forgetting. We uncover novel optimization principles that highlight the potential of forward pass-based methods in mitigating forgetting, managing task conflicts, and reducing memory demands. Additionally, we propose new enhancements that further improve forgetting resistance using only forward passes. This work provides essential tools and insights to advance the development of forward-pass-based methods for continual learning.

• The paper develops ZeroFlow, a benchmark to evaluate gradient-free optimization methods that effectively mitigate catastrophic forgetting.
• It demonstrates that forward-pass approaches can rival traditional backpropagation in accuracy and memory efficiency across datasets like CIFAR-100, CUB, and ImageNet.
• The research offers novel optimization insights and periodic gradient techniques, extending machine learning capabilities in environments without gradient access.

ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think

The research paper titled "ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think" proposes a novel benchmark, named ZeroFlow, which is designed to evaluate gradient-free optimization algorithms for addressing catastrophic forgetting in machine learning models. The work primarily focuses on scenarios where traditional backpropagation is not feasible due to gradient information restrictions, such as in black-box APIs or hardware that does not support backpropagation.

Key Contributions

1. Benchmark Development: ZeroFlow is introduced as the first benchmark that systematically evaluates forward-pass methods and gradient-free optimization techniques for overcoming catastrophic forgetting. This is significant in scenarios where backpropagation and gradient information are inaccessible.
2. Forward Pass Viability: The authors demonstrate that forward-pass methods alone are sufficient to mitigate catastrophic forgetting. This is particularly noteworthy as it challenges the conventional reliance on gradient information in continual learning scenarios.
3. Optimization Insights: The paper provides new principles regarding optimization through forward-pass methods, highlighting their potential in managing task conflicts and optimizing memory demands without backpropagation.
4. Enhanced Techniques: The authors propose novel improvements that further enhance the effectiveness of single forward-pass methods in reducing forgetting. This is achieved through a periodic gradient technique, adding to the arsenal of tools available for efficient model optimization.

Experimental Investigation

• The benchmark evaluates various gradient-free optimization algorithms across different datasets and scenarios of forgetting. The findings show that forward-pass techniques can rival, and sometimes surpass, traditional backpropagation methods in accuracy, average task performance, and memory efficiency.
• Specifically, the methods are tested on datasets like CIFAR-100, CUB, and ImageNet, across different forgetting scenarios, providing comprehensive evidence of their effectiveness.
• The paper presents substantial empirical evidence supporting the use of forward-pass-only methods, underscoring their promise in efficiently overcoming forgetting without the need for gradient calculations. The results indicate competitive average accuracies and reduced forgetting metrics comparable to those achieved by gradient-based counterparts.

Implications and Future Directions

The implications of this research are profound, especially for the development and deployment of artificial intelligence systems in environments where gradient access is restricted. The ability to train and update models effectively with forward-pass methods opens new avenues for model deployment in real-world applications like cloud-based AI solutions or low-power devices.

Future developments may focus on refining the efficiency and scalability of these forward-pass algorithms, particularly as model sizes and complexities continue to grow. Additionally, integrating these methods into existing machine learning frameworks could facilitate broader adoption and enhance the robustness of AI systems in continual learning tasks.

In conclusion, the research paves a promising path towards more adaptable and efficient machine learning models, challenging traditional paradigms by leveraging forward-pass computations to successfully mitigate catastrophic forgetting. This could potentially reshape approaches to training versatile and resilient AI systems in an ever-changing data landscape.