Provable Contrastive Continual Learning (2405.18756v1)

Abstract: Continual learning requires learning incremental tasks with dynamic data distributions. So far, it has been observed that employing a combination of contrastive loss and distillation loss for training in continual learning yields strong performance. To the best of our knowledge, however, this contrastive continual learning framework lacks convincing theoretical explanations. In this work, we fill this gap by establishing theoretical performance guarantees, which reveal how the performance of the model is bounded by training losses of previous tasks in the contrastive continual learning framework. Our theoretical explanations further support the idea that pre-training can benefit continual learning. Inspired by our theoretical analysis of these guarantees, we propose a novel contrastive continual learning algorithm called CILA, which uses adaptive distillation coefficients for different tasks. These distillation coefficients are easily computed by the ratio between average distillation losses and average contrastive losses from previous tasks. Our method shows great improvement on standard benchmarks and achieves new state-of-the-art performance.

• The paper provides rigorous theoretical guarantees linking previous training losses to model performance across sequential tasks.
• It introduces the novel CILA algorithm that employs adaptive distillation coefficients to balance learning plasticity and memory stability.
• Empirical results demonstrate CILA's effectiveness, achieving a 1.77% improvement on Seq-CIFAR-10 and advancing the state-of-the-art.

Provable Contrastive Continual Learning

Abstract Overview

Continual learning, characterized by learning incremental tasks with dynamic data distributions, has traditionally employed a combination of contrastive and distillation losses to achieve notable performance. However, this success lacked theoretical backing until now. The paper "Provable Contrastive Continual Learning" addresses this gap by establishing theoretical performance guarantees for this framework. These guarantees show the boundaries of the model's performance based on the training losses of previous tasks. Inspired by these theoretical insights, the authors propose a novel contrastive continual learning algorithm, CILA, which utilizes adaptive distillation coefficients. The proposed algorithm outperforms existing methods on standard benchmarks, pushing forward the state-of-the-art (SOTA) in the field.

Introduction

Continual learning involves incrementally learning a sequence of tasks while adapting to dynamic data distributions. This necessitates a trade-off between learning plasticity and memory stability—a balance that is challenging due to catastrophic forgetting. Representation-based approaches, particularly those employing contrastive loss, have demonstrated efficacy in mitigating catastrophic forgetting by decoupling representation training from classifier training. Replay-based and regularization-based approaches have provided complementary strategies to sustain performance over recurring tasks. The integration of these approaches into a unified contrastive continual learning framework has empirically shown promise but lacked robust theoretical justification until this work.

Contributions and Theoretical Analysis

The primary contribution of this paper lies in providing theoretical guarantees for contrastive continual learning. The authors analyze how the performance of the model on all seen tasks is bounded by the series of training losses within the framework. Their findings articulate a clear relationship between contrastive losses of consecutive models, elucidating how the final model's population test loss is influenced.

By leveraging these insights, the authors propose CILA, which adopts task-specific adaptive distillation coefficients. These coefficients are computed as the ratio between average distillation losses and average contrastive losses from prior tasks. This adaptive approach moves beyond the static coefficient strategy, aligning more closely with the theoretical guarantees presented.

Results and Implications

Empirical results validate the proposed algorithm's efficacy, showing significant improvements over existing benchmarks across distinct datasets (e.g., Seq-CIFAR-10, Seq-Tiny-ImageNet, R-MNIST). For instance, CILA achieved a 1.77% improvement over the previous SOTA method on Seq-CIFAR-10 with a buffer of 500 samples. These results suggest that adaptive distillation methods, grounded in theoretical analyses, can more effectively balance the retention of past knowledge with the acquisition of new information.

Broader Implications and Future Directions

Theoretical grounding in continual learning, as provided in this work, offers several practical and theoretical implications. Practically, it supports the design of more robust algorithms that adapt dynamically to evolving task sequences, improving memory-stable representations. Theoretically, it opens avenues for further research into adaptive learning mechanisms and their impacts on continual learning performance.

Future research could extend this work by exploring:

1. Different Adaptive Mechanisms: Examining other mechanisms for computing adaptive coefficients that might offer even stronger performance guarantees.
2. Extended Theoretical Analyses: Developing more comprehensive theoretical analyses that encompass a broader range of continual learning scenarios and tasks.
3. Hybrid Approaches: Integrating adaptive methods with other continual learning strategies such as parameter isolation and dynamic architecture adjustments, potentially leading to more versatile and powerful models.

The contributions of this paper provide a solid theoretical and empirical foundation that could significantly influence the future development of robust continual learning systems, facilitating progress towards more intelligent and adaptive AI systems.