Leveraging Implicit and Explicit Regularization in the Function Space to Improve Generalization in Continual Learning

Core Concepts

IMEX-Reg employs a two-pronged approach of implicit regularization using contrastive representation learning and explicit regularization in the function space to improve generalization performance in continual learning under low buffer regimes.

Abstract

The paper proposes IMEX-Reg, a continual learning (CL) approach that leverages both implicit and explicit regularization to mitigate catastrophic forgetting and improve generalization performance. Implicit Regularization: IMEX-Reg uses contrastive representation learning (CRL) as an auxiliary task to learn generalizable features across tasks. The features learned through CRL are shown to be similar to those learned via cross-entropy, benefiting CL under low buffer regimes. Explicit Regularization: IMEX-Reg employs consistency regularization by leveraging the exponential moving average (EMA) of the model to preserve knowledge of previous tasks. It further aligns the geometric structures within the classifier's hypersphere with that of the projection head's hypersphere to compensate for weak supervision under low buffer regimes. Results: IMEX-Reg significantly outperforms rehearsal-based approaches and other state-of-the-art CL methods in various CL scenarios, including Class-IL, Task-IL, and Generalized Class-IL. IMEX-Reg exhibits higher robustness to natural and adversarial corruptions, less task-recency bias, and better model calibration compared to the baselines. Theoretical insights are provided to support the design decisions of IMEX-Reg.

Stats

IMEX-Reg achieves a top-1 accuracy of 48.54% on Seq-CIFAR100 with a buffer size of 200, outperforming ER (21.40%) and DER++ (29.60%). On Seq-TinyImageNet with a buffer size of 500, IMEX-Reg achieves 67.44% accuracy, compared to ER (48.64%) and OCDNet (64.76%). In the Generalized Class-IL (GCIL) setting on CIFAR100, IMEX-Reg achieves 49.07% accuracy with a buffer size of 500, compared to ER (23.62%) and OCDNet (43.58%).

Quotes

"Inspired by how humans leverage inductive biases, we propose IMEX-Reg, a two-pronged implicit (Khosla et al., 2020) - explicit (Arani et al., 2022) regularization approach to mitigate catastrophic forgetting in image classification in CL." "As having the features lie on the unit-hypersphere leads to several desirable traits, we propose a regularization strategy to guide the classifier toward the activation correlations in the unit-hypersphere of the CRL."

Key Insights Distilled From

IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning

by Prashant Bha... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18161.pdf

Deeper Inquiries

How can the proposed IMEX-Reg approach be extended to other domains beyond image classification, such as natural language processing or reinforcement learning

The IMEX-Reg approach can be extended to other domains beyond image classification by adapting the implicit-explicit regularization framework to suit the specific characteristics of those domains. For natural language processing (NLP), the implicit regularization component could involve leveraging pre-trained language models like BERT or GPT-3 to learn generalizable features across tasks. This could be complemented by explicit regularization techniques that focus on aligning the geometric structures of word embeddings or sentence representations. In reinforcement learning (RL), the implicit regularization could involve using auxiliary tasks like state prediction or reward prediction to encourage the learning of generalizable features. The explicit regularization could focus on aligning the decision boundaries in the action space to improve generalization across tasks in RL settings.

What are the potential limitations of the Johnson-Lindenstrauss lemma-based explicit regularization approach, and how can it be further improved

One potential limitation of the Johnson-Lindenstrauss lemma-based explicit regularization approach is the assumption of linearity in the mapping function between the high-dimensional and low-dimensional spaces. This assumption may not hold in complex data distributions where non-linear relationships are prevalent. To address this limitation, the approach can be further improved by incorporating non-linear mapping functions, such as neural networks or kernel methods, to capture the intricate relationships between the high-dimensional representations and the unit hypersphere. Additionally, exploring adaptive regularization techniques that dynamically adjust the regularization strength based on the data distribution could enhance the performance of the explicit regularization approach.

Can the implicit and explicit regularization techniques in IMEX-Reg be combined with other CL strategies, such as task-aware or task-agnostic approaches, to achieve even better performance

The implicit and explicit regularization techniques in IMEX-Reg can be combined with other CL strategies, such as task-aware or task-agnostic approaches, to achieve even better performance in continual learning tasks. Task-aware approaches could involve incorporating task-specific information into the regularization framework to adapt the regularization strategy based on the characteristics of each task. Task-agnostic approaches, on the other hand, could focus on generalizing the regularization techniques across all tasks to promote overall model stability and robustness. By integrating these strategies with the implicit-explicit regularization framework of IMEX-Reg, the model can benefit from a more comprehensive regularization scheme that addresses both task-specific nuances and generalization challenges in continual learning scenarios.