Revisiting Flatness-Aware Optimization in Continual Learning With Orthogonal Gradient Projection
Abstract
The goal of continual learning (CL) is to learn from a series of continuously arriving new tasks without forgetting previously learned old tasks. To avoid catastrophic forgetting of old tasks, orthogonal gradient projection (OGP) based CL methods constrain the gradients of new tasks to be orthogonal to the space spanned by old tasks. This strict gradient constraint will limit the learning ability of new tasks, resulting in lower performance on new tasks. In this paper, we first establish a unified framework for OGP-based CL methods. We then revisit OGP-based CL methods from a new perspective on the loss landscape, where we find that when relaxing projection constraints to improve performance on new tasks, the unflatness of the loss landscape can lead to catastrophic forgetting of old tasks. Based on our findings, we propose a new Dual Flatness-aware OGD framework that optimizes the flatness of the loss landscape from both data and weight levels. Our framework consists of three modules: data and weight perturbation, flatness-aware optimization, and gradient projection. Specifically, we first perform perturbations on the task's data and current model weights to make the task's loss reach the worst-case. Next, we optimize the loss and loss landscape on the original data and the worst-case perturbed data to obtain a flatness-aware gradient. Finally, the flatness-aware gradient will update the network in directions orthogonal to the space spanned by the old tasks. Extensive experiments on four benchmark datasets show that the framework improves the flatness of the loss landscape and performance on new tasks, and achieves state-of-the-art (SOTA) performance on average accuracy across all tasks.
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DFGP reinterprets the problem of catastrophic forgetting in orthogonal projection from the perspective of loss landscape flatness, and accordingly improves network flatness to balance plasticity and stability.
“DFGP (Yang et al., 2023, 2025) reinterprets the problem of catastrophic forgetting in orthogonal projection from the perspective of loss landscape flatness, and accordingly improves network flatness to balance plasticity and stability.”
From Knowledge Decoupling via Orthogonal Projection for Lifelong Editing of Large Language Models (2025) · cites this paper as Yang et al., 2025
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- Authors
- Yang, E., Shen, L., Wang, Z., Liu, S., Guo, G., Wang, X., & Tao, D.
- Affiliations
- Northeastern University (CN)
- Sun Yat-sen University (CN)
- University of Maryland, College Park (US)
- University of Oxford (GB)
- Nanyang Technological University (SG)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2025-05
- Volume
- 47
- Issue
- 5
- Pages
- 3895-3907
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W4407168958
- Cited by
- 5
- References
- 76
- Citations by year
- 2026: 5
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (62032013)
- Science and technology projects in Liaoning Province (2023JH3/10200005)
- Natural Science Foundation (JCYJ20241202124430041)
- National Research Foundation Singapore (NRF-P2024-001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- continual learning, catastrophic forgetting, loss landscape flatness, worst-case perturbations
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