Slow and Fast Parameter-Efficient Tuning (SAFE) for Continual Learning with Pre-Trained Models

核心概念

SAFE, a novel continual learning framework, leverages the strengths of both slow and fast learners with parameter-efficient tuning to effectively transfer knowledge from pre-trained models and adapt to new information without catastrophic forgetting.

摘要

• Bibliographic Information: Zhao, L., Zhang, X., Yan, K., Ding, S., & Huang, W. (2024). SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models. Advances in Neural Information Processing Systems, 38.

• Research Objective: This paper introduces SAFE, a novel framework designed to address the limitations of existing continual learning methods that struggle to effectively transfer knowledge from pre-trained models (PTMs) and suffer from catastrophic forgetting.

• Methodology: SAFE employs a dual-learner system: a slow learner (SL) and a fast learner (FL), both utilizing parameter-efficient tuning (PET). In the initial phase, the SL is trained to inherit general knowledge from the PTM through a knowledge transfer loss, maximizing feature correlation while minimizing redundancy. Subsequently, the SL parameters are frozen, and only the classification weights are updated. In contrast, the FL, with trainable parameters, focuses on incorporating new concepts in subsequent sessions, guided by the SL through feature alignment and cross-classification loss to mitigate forgetting. During inference, an entropy-based aggregation strategy dynamically combines the predictions of both learners, leveraging their complementary strengths.

• Key Findings: Extensive experiments on seven benchmark datasets, including CIFAR100, ImageNet-R, ImageNet-A, CUB200, Omnibenchmark, VTAB, and DomainNet, demonstrate SAFE's superior performance. Notably, SAFE achieves state-of-the-art results, significantly surpassing the second-best method on ImageNet-A by 4.4%.

• Main Conclusions: SAFE effectively addresses the stability-plasticity dilemma in continual learning by combining the strengths of slow and fast learners. The framework's ability to transfer knowledge from PTMs and adapt to new information without catastrophic forgetting makes it a significant contribution to the field.

• Significance: SAFE offers a promising solution for developing more robust and adaptable continual learning systems, particularly in image recognition tasks. Its effectiveness in leveraging pre-trained models and mitigating forgetting opens up new possibilities for real-world applications where continuous learning is essential.

• Limitations and Future Research: While SAFE demonstrates impressive results, it relies on a strong feature extractor inherited from the PTM, potentially limiting its applicability when starting from scratch or with small initial tasks. Future research could explore methods to enhance the framework's flexibility in such scenarios. Additionally, investigating alternative aggregation strategies and exploring the periodic updating of the slow learner could further improve SAFE's performance and adaptability.

统计

SAFE surpasses the second-best result on ImageNet-A by over 4%.
SAFE improves the average accuracy over six datasets by 2.1% compared to the previous best approach.
SAFE outperforms the second-best result on DomainNet by 1.2%.

引用

"To inherit the generalizable knowledge in PTMs that has been overlooked in existing continual learning works, we propose to explicitly transfer knowledge from the PTM to a slow learner. Once trained, the slow learner can generalize well to classes in incremental sessions."
"For improving the plasticity of CL models, we include a fast learner with guidance from the slow learner to continuously incorporate novel concepts. Moreover, by aggregating both slow and fast learners into a unified framework SAFE, robust predictions can be further made."
"The superiority of SAFE is validated on seven continual learning datasets where our method consistently achieves remarkable state-of-the-art performance."

从中提取的关键见解

SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models

by Linglan Zhao... 在 arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.02175.pdf

更深入的查询

How might SAFE be adapted for other domains beyond image recognition, such as natural language processing or reinforcement learning?

SAFE's core principles of leveraging pre-trained models and balancing stability and plasticity for continual learning hold promise for adaptation to other domains like natural language processing (NLP) and reinforcement learning (RL). Here's how:
NLP:

Pre-trained Language Models (PLMs): Instead of image-focused PTMs like ViT, SAFE can leverage PLMs like BERT, RoBERTa, or GPT variants. These models provide rich textual representations.
Parameter-Efficient Tuning: Similar to adapters and prompts in vision, NLP has techniques like adapter layers, prompt tuning, and prefix tuning [10] that enable efficient adaptation of PLMs without massive fine-tuning. SAFE can incorporate these.
Continual Learning Tasks: NLP tasks like sentiment analysis, question answering, or machine translation can be framed for continual learning. For example, a model could incrementally learn to translate between new pairs of languages.
Slow and Fast Learners: The concept of slow and fast learners can be applied to different components of the NLP model. For instance, the slow learner could focus on general language understanding encoded in the PLM, while the fast learner specializes in the task-specific aspects.
RL:

Pre-trained Representations: While less prevalent than in NLP or vision, pre-training RL agents on diverse environments is gaining traction. SAFE could leverage these pre-trained agents as a starting point.
Continual Learning in RL: Continual learning is crucial for RL agents operating in dynamic environments. SAFE's principles can be applied to enable agents to acquire new skills without forgetting old ones.
Challenges: Adapting SAFE to RL presents unique challenges:

Non-stationary Environments: RL environments often change over time, making it harder to define distinct tasks or sessions.
Exploration-Exploitation Dilemma: Balancing the need to exploit learned knowledge and explore new possibilities is crucial in RL.
Reward Function Design: Continual learning in RL often requires carefully designing reward functions that encourage both knowledge retention and new skill acquisition.
Key Considerations for Adaptation:

Domain-Specific PET Techniques: Identifying and utilizing the most effective parameter-efficient tuning methods for the target domain is essential.
Task Formulation: Carefully defining the continual learning tasks and how new knowledge is introduced is crucial for successful adaptation.
Evaluation Metrics: Appropriate evaluation metrics that capture both knowledge retention and new task performance are essential for assessing continual learning progress.

Could the reliance on a strong pre-trained model be a limiting factor for SAFE in scenarios with limited data or computational resources?

Yes, SAFE's reliance on a strong pre-trained model (PTM) can be a limiting factor in scenarios with limited data or computational resources.
Limited Data:

PTM Bias: PTMs are trained on massive datasets, which may not reflect the nuances of a smaller, domain-specific dataset. This can lead to the model being biased towards the PTM's knowledge distribution and struggling to learn effectively from the limited data.
Overfitting: With limited data, there's a higher risk of overfitting to the PTM's knowledge, hindering the model's ability to generalize to unseen examples.
Computational Resources:

PTM Size: Large PTMs require significant computational resources for fine-tuning and even for inference. This can be prohibitive for researchers or developers with limited access to powerful hardware.
Training Time: Even with parameter-efficient tuning, adapting a PTM takes longer than training a smaller model from scratch. This can be a bottleneck in time-sensitive applications.
Alternatives and Mitigations:

Smaller PTMs: Consider using smaller, more specialized PTMs that are less resource-intensive.
Transfer Learning from Related Domains: If a PTM for the specific domain is unavailable, explore transferring knowledge from a related domain where a PTM exists.
Data Augmentation: Increase the effective size of the limited dataset through data augmentation techniques to reduce overfitting.
Model Compression: Techniques like knowledge distillation can compress the knowledge of a large PTM into a smaller, more efficient model.
Balancing Act:
It's important to strike a balance between leveraging the power of PTMs and working within resource constraints. Carefully evaluating the trade-offs and exploring alternative approaches is crucial in such scenarios.

What are the ethical implications of developing increasingly sophisticated continual learning systems, and how can SAFE be designed to address potential biases?

Developing increasingly sophisticated continual learning systems, while promising, raises important ethical considerations, particularly regarding bias:
Potential Biases:

Data Bias Amplification: Continual learning systems, like SAFE, can inherit and even amplify biases present in the training data. If the initial data or subsequent data streams contain biased representations, the model's decisions can perpetuate and worsen these biases over time.
Catastrophic Forgetting and Fairness: As models learn new information, they risk forgetting previously learned knowledge. This forgetting can disproportionately impact certain groups or classes, leading to unfair or discriminatory outcomes. For example, a model might become less accurate in recognizing faces of underrepresented groups if the data distribution shifts.
Lack of Transparency: The dynamic nature of continual learning can make it challenging to understand how a model arrived at a particular decision. This lack of transparency can hinder accountability and make it difficult to identify and address biases.
Designing SAFE to Address Biases:

Diverse and Representative Data: Ensuring the initial PTM and subsequent data streams are diverse and representative is crucial. This involves actively seeking out data that reflects a wide range of demographics, perspectives, and experiences.
Bias Mitigation Techniques: Incorporate bias mitigation techniques into SAFE's training process. This could involve:

Adversarial Training: Training the model to be robust to adversarial examples that exploit biased predictions.
Fairness Constraints: Adding constraints to the loss function that penalize the model for making biased decisions.

Monitoring and Auditing: Continuously monitor SAFE's performance across different subgroups to detect and address emerging biases. Regular audits can help identify and mitigate potential issues.
Explainability and Interpretability: Develop methods to explain SAFE's decision-making process, making it easier to understand how the model arrived at a particular prediction and identify potential sources of bias.
Human Oversight and Intervention: Recognize that continual learning systems are not perfect and require human oversight. Establish mechanisms for human intervention to correct biased outcomes and ensure fairness.
Ethical Development is Crucial:
As we develop increasingly sophisticated continual learning systems, it's essential to prioritize ethical considerations. By proactively addressing potential biases and promoting fairness, transparency, and accountability, we can harness the power of these systems while mitigating potential harms.