Continual Lifelong Learning with Neural Networks: A Review (1802.07569v4)

Abstract: Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval. Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information. However, lifelong learning remains a long-standing challenge for machine learning and neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. In this review, we critically summarize the main challenges linked to lifelong learning for artificial learning systems and compare existing neural network approaches that alleviate, to different extents, catastrophic forgetting. We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsic motivation, and multisensory integration.

Continual Lifelong Learning with Neural Networks: A Review

The paper, "Continual Lifelong Learning with Neural Networks: A Review" by German I. Parisi et al., delivers a comprehensive analysis of lifelong learning in computational systems, primarily focusing on neural network models. Lifelong learning is a fundamental characteristic of human and animal intelligence, enabling the continual acquisition, refinement, and transfer of knowledge across various tasks and over extended periods. However, translating this capability to artificial systems remains a significant challenge, predominantly due to the phenomenon of catastrophic forgetting.

Key Challenges in Lifelong Learning

Catastrophic forgetting occurs when new information acquisition negatively impacts previously learned knowledge. Traditional neural network models, typically designed for stationary data distributions, are particularly vulnerable to this phenomenon. The authors identify and critically examine the major challenges associated with lifelong learning, emphasizing the need for models that can balance plasticity and stability—termed the stability-plasticity dilemma. While plasticity is essential for integrating new information, stability is necessary to protect and consolidate existing knowledge.

Biological Principles and Computational Models

The paper explores various biological mechanisms that support lifelong learning in mammals, such as structural plasticity, memory replay, and complementary learning systems (CLS). These principles inspire several computational approaches aimed at mitigating catastrophic forgetting. The authors categorize these approaches into three main strategies:

1. Regularization Approaches: These methods involve adding constraints to the learning process to protect previously acquired knowledge. Techniques such as Elastic Weight Consolidation (EWC) and intrinsic synaptic plasticity adjustments are discussed. While effective to some extent, these methods may lead to a trade-off between the performance on old and new tasks.
2. Dynamic Architectures: Methods in this category dynamically adjust the network's structure, either by adding new neurons/layers or by reallocating resources. Examples include Progressive Neural Networks and the Neural Turing Machine. These approaches effectively mitigate catastrophic forgetting at the cost of increased computational complexity and resource management challenges.
3. Complementary Learning Systems and Memory Replay: Inspired by the interplay of the hippocampus and neocortex in biological systems, these methods use dual-memory architectures to separate fast-learning episodic memory from slow-learning semantic memory. Generative replay techniques and models like the Gradient Episodic Memory (GEM) fall under this category. These methods show promise in balancing the learning of new tasks while retaining old knowledge.

Benchmarking and Evaluation

The authors highlight the importance of rigorous benchmarking and standardized evaluation metrics to assess the efficacy of lifelong learning models. They critique the over-reliance on simple datasets like MNIST and propose more comprehensive evaluation schemes involving datasets such as CUB-200 and CORe50, which present more complex scenarios that better reflect real-world conditions.

Practical and Theoretical Implications

The implications of robust lifelong learning models are profound for the development of autonomous agents and robots capable of learning in dynamic environments. Practically, these models can lead to more adaptive and intelligent systems that can handle continuous streams of non-stationary data without the need for re-training from scratch. Theoretically, advancing lifelong learning models contributes to our understanding of the principles underlying biological learning systems and their potential applications in artificial intelligence.

Future Directions

The paper concludes by suggesting future research directions. These include developing more sophisticated models that integrate biological principles of structural plasticity and memory consolidation, designing better benchmarks and evaluation metrics, and exploring the interplay between different learning paradigms such as curriculum learning, intrinsic motivation, and crossmodal learning.

In summary, Parisi et al. provide a thorough and critical review of the current state of lifelong learning in neural networks, offering valuable insights into both the challenges and promising approaches. Their work underscores the importance of interdisciplinary research and rigorous evaluation to advance the development of robust lifelong learning systems.