Continual Learning Should Move Beyond Incremental Classification (2502.11927v1)

Abstract: Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental classification tasks, where models learn to classify new categories while retaining knowledge of previously learned ones. Here, we argue that maintaining such a focus limits both theoretical development and practical applicability of CL methods. Through a detailed analysis of concrete examples - including multi-target classification, robotics with constrained output spaces, learning in continuous task domains, and higher-level concept memorization - we demonstrate how current CL approaches often fail when applied beyond standard classification. We identify three fundamental challenges: (C1) the nature of continuity in learning problems, (C2) the choice of appropriate spaces and metrics for measuring similarity, and (C3) the role of learning objectives beyond classification. For each challenge, we provide specific recommendations to help move the field forward, including formalizing temporal dynamics through distribution processes, developing principled approaches for continuous task spaces, and incorporating density estimation and generative objectives. In so doing, this position paper aims to broaden the scope of CL research while strengthening its theoretical foundations, making it more applicable to real-world problems.

• The paper shows that an overemphasis on incremental classification limits the development of versatile continual learning methods.
• It identifies key challenges including modeling continuous changes, choosing appropriate similarity metrics, and moving past classification objectives.
• It recommends formalizing temporal dynamics and integrating generative tasks to enhance adaptability and mitigate catastrophic forgetting.

Moving Beyond Incremental Classification in Continual Learning

The paper "Continual Learning Should Move Beyond Incremental Classification" presents a critical examination of the current dominant paradigm in continual learning (CL) research, which focuses on incremental classification tasks. The authors assert that the narrow focus on classification limits both theoretical advancements and practical applications of continual learning systems. The paper argues for a broader scope, identifying fundamental challenges associated with this expansion and proposing recommendations to overcome these limitations.

Critical Assessment of Current Continual Learning

Continual Learning is traditionally explored through incremental classification tasks, often by partitioning benchmark datasets into disjoint subsets, each representing a task. This setup is advantageous due to its simplicity and reproducibility, allowing direct comparisons between different methods. However, the authors question the assumption that solutions from this setup generalize well to more complex, real-world applications.

A critical examination reveals that focusing solely on classification impairs the development of more general CL methods. The authors highlight the limitations of this approach by discussing various scenarios where CL needs extend beyond simple task classification. These include multi-target classification, constrained robotics, continuous task domains, and higher-level concept memorization. In each case, they note how standard CL methods often struggle to adapt effectively.

Fundamental Challenges Beyond Classification

The authors identify three key conceptual challenges that arise when broadening the scope of CL:

1. Nature of Continuity in Learning Problems: Continual learning problems often feature continuous changes, both temporally and in the underlying task space. Unlike discrete task boundaries, these require more sophisticated approaches to modeling and knowledge retention.
2. Choice of Spaces and Metrics: Appropriate choices of spaces and metrics to measure similarity are crucial. Many current methods do not explicitly consider the impact of these choices, leading to scenarios where naive implementations fail, especially in constrained or structured prediction tasks.
3. Beyond Classification Objectives: Most current CL methods are heavily focused on conditional classification learning, which may not be sufficient for complex tasks that involve exploration, strategy, and memory of abstract concepts. Generative and density estimation models could play a significant role but are often overlooked.

Recommendations for Future Research

To address these challenges, the paper offers several recommendations meant to guide future work in continually learning:

• Formalize Temporal Dynamics: Developing models of learning processes as continuous distribution processes can help better capture changing dynamics over time, paving the way for improved adaptation to non-stationary environments.
• Explore Continuous Task Spaces: Research should explore methods that effectively handle continuous task identities and structures beyond discrete classes, encouraging flexibility and better real-world applicability.
• Incorporate Generative and Density-Based Objectives: By integrating generative tasks and density estimation into CL systems, researchers can potentially alleviate issues related to catastrophic forgetting, task transition detection, and overall robustness of learning methods.

Implications and Future Directions

The implications of broadening continual learning frameworks are significant both for practical applications and theoretical advancements. By moving beyond incremental classification, CL systems can potentially cover a more diverse set of tasks with improved adaptability to dynamic and complex real-world environments. The recommendations provided could guide researchers in developing CL models with greater generalization capability and robustness.

Looking ahead, the paper suggests that examining and addressing the foundational assumptions of current CL methods in the face of new challenges could result in more nuanced and effective learning systems. Models that integrate different types of knowledge, including generative capabilities and a broader understanding of tasks, will likely prove essential for future AI developments.