Gradient based sample selection for online continual learning (1903.08671v5)

Abstract: A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous works often depend on task boundary and i.i.d. assumptions to properly select samples for the replay buffer. In this work, we formulate sample selection as a constraint reduction problem based on the constrained optimization view of continual learning. The goal is to select a fixed subset of constraints that best approximate the feasible region defined by the original constraints. We show that it is equivalent to maximizing the diversity of samples in the replay buffer with parameters gradient as the feature. We further develop a greedy alternative that is cheap and efficient. The advantage of the proposed method is demonstrated by comparing to other alternatives under the continual learning setting. Further comparisons are made against state of the art methods that rely on task boundaries which show comparable or even better results for our method.

Gradient Based Sample Selection for Online Continual Learning

The paper "Gradient Based Sample Selection for Online Continual Learning" addresses the critical challenge of catastrophic forgetting in online continual learning systems. Continual learning involves processing a non-stationary, continuous stream of data, which can lead to the overwriting of previously acquired knowledge in neural networks. The authors propose a novel methodology for sample selection in replay buffers to mitigate forgetting without relying on task boundaries.

Key Contributions

1. Constraint Reduction Formulation: The authors conceptualize the sample selection process as a constraint reduction problem via a constrained optimization framework. They strive to approximate the feasible region defined by these constraints by selecting a diverse subset of data points based on their parameter gradients.
2. Surrogate Objective: The paper presents a surrogate that simplifies the solid angle minimization problem associated with selecting representative samples in high-dimensional spaces. This surrogate aims to maximize the diversity of gradient directions, ensuring a comprehensive representation of previous experience.
3. Efficient Greedy Algorithm: To address scalability concerns, the authors introduce a computationally efficient greedy algorithm. This method mimics reservoir sampling in ease while improving balance in representation across diverse data streams, addressing common pitfalls in existing methods that presume iid data distribution.
4. Empirical Validation: The proposed method is rigorously compared against existing strategies, including GEM and iCaRL, showcasing competitive performance without requiring task boundaries or iid assumptions.

Implications

• Practical Enhancements: The methodology supports practical applications where task boundaries are not clearly defined, making it broadly applicable in dynamic environments like robotics or real-time data analytics.
• Computational Efficiency: By leveraging a greedy approach, the solution is viable for large-scale and resource-constrained applications, broadening the accessibility of continual learning systems.
• Theory and Application: The formalization of the problem as constraint reduction offers a novel perspective, potentially influencing future theoretical work in continual learning and optimization.

Future Directions

Continued exploration could focus on refining the surrogate objective for even more precise alignment with solid angle minimization. Additionally, integrating such approaches with adaptive architectures or meta-learning frameworks might further enhance learning efficiency. The paper opens avenues for diverse applications, encouraging the development of robust continual learning systems that can efficiently adapt to unstructured data over time.

In summary, this work presents a thoughtfully constructed approach to a fundamental problem in continual learning, offering practical solutions and encouraging future innovation in adaptive machine learning paradigms.