Addressing the Stability-Plasticity Dilemma in Continual Learning with Recall-Oriented Framework

Основні поняття

The author proposes a recall-oriented continual learning framework to balance stability and plasticity by separating mechanisms for acquiring new knowledge and recalling past knowledge effectively.

Анотація

The paper introduces a novel recall-oriented continual learning framework to tackle the stability-plasticity dilemma in continual learning. By analyzing representation complexity, the study highlights the effectiveness of maintaining parameter-level representations for preserving knowledge. Experimental results demonstrate that the proposed framework outperforms existing methods in both task-aware and task-agnostic scenarios.
Key points:

Introduction of recall-oriented continual learning framework.
Analysis on representation complexity favoring parameter-level representations.
Experimental results showing superior performance over existing methods.

Статистика

"Our framework achieves almost zero-forgetting with its near-zero BWT values."
"GAMM shows better efficiency in memory usage compared to other replay-based methods."
"GAMM manages to achieve the best performance not only in task-aware scenarios but also in task-agnostic scenarios."

Цитати

"The major challenge is how to train GAMM efficiently and scalably."
"Our strategy is to use Bayesian neural network (BNN) as a task-specific model without redundant training."
"GAMM is trained to generate learned parameters for the current task using the trained BNN."

Ключові висновки, отримані з

Recall-Oriented Continual Learning with Generative Adversarial Meta-Model

by Haneol Kang,... о arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.03082.pdf

Глибші Запити

How can the recall-oriented framework be applied beyond computer science?

The recall-oriented framework's principles can be extended to various fields beyond computer science, such as education, psychology, and healthcare. In education, this framework could be utilized to enhance learning retention by separating new knowledge acquisition from long-term memory consolidation. In psychology, it could aid in understanding human memory processes better and potentially improve therapies for memory-related disorders. Additionally, in healthcare, a similar approach could be used to optimize patient treatment plans by ensuring that past medical knowledge is retained while incorporating new information effectively.

What counterarguments exist against prioritizing parameter-level representations?

One counterargument against prioritizing parameter-level representations is the potential loss of contextual information present in input or feature-level data. By focusing solely on model parameters, there may be a risk of oversimplification or abstraction that disregards nuances captured at lower levels of representation. Additionally, some critics may argue that emphasizing parameter-level representations could lead to a disconnect from the original data distribution and limit the model's ability to adapt flexibly to diverse inputs or tasks.

How does the study's focus on generative replay impact long-term memory retention?

The study's emphasis on generative replay plays a crucial role in enhancing long-term memory retention within the recall-oriented framework. By utilizing generative models like GAMM to recreate task-specific models during inference time, the system ensures that past knowledge remains accessible without interference from new learning experiences. This approach helps maintain a balance between stability and plasticity by allowing for efficient recall of previous tasks' information when needed. Ultimately, generative replay contributes significantly to preserving learned knowledge over an extended period and improving overall performance in continual learning scenarios.