Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality (2310.07234v1)

Abstract: Prompt-based continual learning is an emerging direction in leveraging pre-trained knowledge for downstream continual learning, and has almost reached the performance pinnacle under supervised pre-training. However, our empirical research reveals that the current strategies fall short of their full potential under the more realistic self-supervised pre-training, which is essential for handling vast quantities of unlabeled data in practice. This is largely due to the difficulty of task-specific knowledge being incorporated into instructed representations via prompt parameters and predicted by uninstructed representations at test time. To overcome the exposed sub-optimality, we conduct a theoretical analysis of the continual learning objective in the context of pre-training, and decompose it into hierarchical components: within-task prediction, task-identity inference, and task-adaptive prediction. Following these empirical and theoretical insights, we propose Hierarchical Decomposition (HiDe-)Prompt, an innovative approach that explicitly optimizes the hierarchical components with an ensemble of task-specific prompts and statistics of both uninstructed and instructed representations, further with the coordination of a contrastive regularization strategy. Our extensive experiments demonstrate the superior performance of HiDe-Prompt and its robustness to pre-training paradigms in continual learning (e.g., up to 15.01% and 9.61% lead on Split CIFAR-100 and Split ImageNet-R, respectively). Our code is available at \url{https://github.com/thu-ml/HiDe-Prompt}.

Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality

This paper investigates prompt-based continual learning and its reliance on hierarchical decomposition to address sub-optimality in self-supervised pre-training scenarios. Prompt-based methodologies have been recognized for their efficacy in leveraging pre-trained models for continual learning tasks. However, empirical evidence presented in this paper indicates a significant gap between expected and realized performance when these methods are applied after self-supervised pre-training.

Key Insights and Contributions

The authors conduct a theoretical analysis, decomposing the continual learning objective into hierarchical components: within-task prediction (WTP), task-identity inference (TII), and task-adaptive prediction (TAP). This decomposition provides a structured framework for understanding and addressing inefficiencies in current prompt-based strategies.

Key insights include:

1. Performance Under Self-Supervised Pre-Training: Contrary to successful outcomes in supervised pre-training, current prompt-based continual learning approaches reveal sub-optimal performance when models are pre-trained in a self-supervised manner. This is attributed to difficulties in integrating task-specific knowledge within pre-training representations.
2. Hierarchical Decomposition (HiDe-)Prompt: The introduction of HiDe-Prompt as a remedy to leverage hierarchical components for optimized continual learning. This involves using ensembles of task-specific prompts and hierarchical optimization strategies such as contrastive regularization.
3. Empirical Results: The HiDe-Prompt presented superior performance over traditional methods in experimentation. For instance, it achieved up to a 15.01% improvement in accuracy on Split CIFAR-100 compared to existing prompt-based methodologies.
4. Robustness Across Pre-Training Paradigms: The experiments demonstrate HiDe-Prompt's robustness across varying pre-training paradigms, whether supervised or self-supervised, highlighting its potential as a versatile approach to continual learning.

Implications and Future Directions

The implications of this research extend to the theoretical and practical realms of AI, providing critical insights into the deployment and optimization of prompt-based learning systems in environments characterized by abundant unlabeled data. HiDe-Prompt's design emphasizes the importance of statistical modeling of representations to enhance task specificity and efficiency in continual learning setups.

Future developments in AI could look into further refining hierarchical optimization techniques or exploring other forms of parameter-efficient fine-tuning methods such as adapters or LoRA within the context of self-supervised learning. As the field progresses, integrating neuro-inspired adaptability mechanisms for optimized continual learning could offer novel avenues of research.

By elucidating the complexities of prompt-based continual learning, particularly under self-supervised conditions, this paper lays the groundwork for advancements in the efficiency and robustness of learning algorithms capable of handling dynamic and data-heavy environments.