Learning to Continually Learn (2002.09571v2)

Abstract: Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work has gone towards preventing the default tendency of machine learning models to catastrophically forget, yet virtually all such work involves manually-designed solutions to the problem. We instead advocate meta-learning a solution to catastrophic forgetting, allowing AI to learn to continually learn. Inspired by neuromodulatory processes in the brain, we propose A Neuromodulated Meta-Learning Algorithm (ANML). It differentiates through a sequential learning process to meta-learn an activation-gating function that enables context-dependent selective activation within a deep neural network. Specifically, a neuromodulatory (NM) neural network gates the forward pass of another (otherwise normal) neural network called the prediction learning network (PLN). The NM network also thus indirectly controls selective plasticity (i.e. the backward pass of) the PLN. ANML enables continual learning without catastrophic forgetting at scale: it produces state-of-the-art continual learning performance, sequentially learning as many as 600 classes (over 9,000 SGD updates).

An Analysis of "Learning to Continually Learn"

Abstract and Overview

The paper "Learning to Continually Learn" presents an innovative approach to address the enduring challenge of catastrophic forgetting in deep neural networks through continual learning. The authors propose a meta-learning framework that diverges from manually designed solutions by introducing A Neuromodulated Meta-Learning Algorithm (ANML). ANML is a novel neural architecture inspired by neuromodulatory processes in the brain, which differentiates through a sequential learning process to meta-learn an activation-gating function. Specifically, a neuromodulatory (NM) network gates the activations of a prediction learning network (PLN), facilitating context-dependent selective activation. This architecture supports selective plasticity within the model, enabling it to learn serial tasks without catastrophic forgetting effectively.

Background and Motivation

Catastrophic forgetting is a significant hurdle in machine learning, where acquiring new information results in overwriting previously learned knowledge, thus degrading model performance on past tasks. Traditional solutions have included replay methods, such as interleaving old and new data, and regularization techniques that restrict parameter updates. However, these methods often involve hand-crafted heuristics and are not scalable or universally applicable.

The paper aims to transcend these limitations by leveraging the potential of meta-learning, which seeks to learn an effective continual learning strategy inherently. By using neural networks modeled on biological processes, ANML autonomously discovers optimal mechanisms to allocate representation and storage in neural pathways, promoting efficient sequential learning.

Methodology and Core Contributions

The central contribution of the paper is ANML, which integrates a neuromodulatory network with a standard prediction network. The NM network learns to gate the activations selectively in the PLN, permitting only relevant subsets of the network to activate for specific inputs—thereby minimizing interference and preserving past knowledge. In the meta-learning outer loop, ANML learns network can extend across 600 sequential tasks, achieving 9,000 Stochastic Gradient Descent (SGD) updates without catastrophic forgetting.

The meta-learning is operationalized using a meta-training phase on the Omniglot dataset, a collection of character recognition tasks where ANML demonstrates substantial improvement over prior meta-learning frameworks like OML (Online aware Meta-Learning). Unlike OML, ANML does not require explicit task information or auxiliary losses like sparsity to reduce forgetting.

Results and Discussion

In extensive empirical evaluations, ANML achieves state-of-the-art performance on continual learning tasks, significantly outperforming both traditional methods and current benchmarks such as OML. It maintains superior accuracy across sequences of hundreds of tasks, substantiating its robust mitigation of catastrophic forgetting. The model retains information effectively, resulting in only a 10% drop in performance when compared to its oracle-i.i.d-trained counterpart, which is a critical milestone in the field.

The high accuracy achieved by ANML, even when data is not shuffled and observed continually, demonstrates its capability to substantially preserve knowledge over extended tasks. The method's design thereby holds implications for developing AI capable of lifelong learning, potentially applicable to real-world scenarios involving robots, autonomous systems, and complex data streams.

Future Directions and Implications

Moving forward, the ANML framework provides a pathway for augmenting neural architectures with neuromodulatory processes to enhance learning efficiency in a sequential context. Potential avenues for development include extending ANML to larger, more complex tasks beyond Omniglot, and integrating it with reinforcement learning to validate its scalability across diverse domains.

Overall, the work exemplifies the shift towards leveraging meta-learning to solve AI's grand challenges, supporting strategies like AI-generating algorithms that aim to algorithmically discover optimal solutions for AI systems. The promising results from ANML contribute to this evolving landscape, reinforcing the trend of achieving improved learning outcomes by transitioning from manual to automated design processes in AI research.

By synthesizing biological inspiration with advanced machine learning techniques, ANML represents a step towards creating more adaptable, resilient, and intelligent learning systems capable of thriving in dynamic environments.