AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computer Science

Background:

• Continual learning faces challenges with heterogeneous datasets varying in complexity, size, and similarity.
• Existing task-agnostic methods like rehearsal and regularization have limitations in handling such variations.
• A need exists for adaptive strategies to manage diverse data in sequential learning environments.

Purpose of the Study:

• To introduce a novel adaptive continual learning (AdaptCL) method designed for heterogeneous sequential datasets.
• To address the limitations of conventional methods in managing data complexity, size, and similarity variations.
• To demonstrate the robustness and general applicability of AdaptCL across diverse learning scenarios.

Main Methods:

• Proposed AdaptCL method utilizing fine-grained data-driven pruning for adaptability to data complexity and size.
• Employed task-agnostic parameter isolation to mitigate catastrophic forgetting influenced by data similarity.
• Evaluated AdaptCL through a two-pronged case study on MNIST variants, DomainNet, and diverse large-scale and few-shot datasets.

Main Results:

• AdaptCL demonstrated consistent and robust performance across all evaluated heterogeneous datasets.
• The method effectively adapted to variations in dataset complexity, size, and similarity.
• Parameter isolation successfully reduced catastrophic forgetting in scenarios with differing data similarities.

Conclusions:

• AdaptCL offers a flexible and generally applicable solution for continual learning with heterogeneous datasets.
• The proposed adaptive approach overcomes limitations of traditional rehearsal and regularization techniques.
• AdaptCL shows significant potential for real-world applications involving sequential and diverse data streams.