Preserving Generic Knowledge in Continual Learning of Vision-Language Foundation Models

Core Concepts

A novel approach that selectively updates a sparse set of parameters in vision-language foundation models to expand their knowledge while preserving their original capabilities.

Abstract

The content discusses a method for continually updating large pre-trained vision-language foundation models, such as CLIP, to accommodate new information while retaining their original capabilities. The key insights are: Foundation models have initial knowledge on various tasks and domains, which can be leveraged to guide the update process. Instead of updating all parameters equally, the proposed Selective Parameter Update (SPU) method localizes the updates to a sparse set of parameters relevant to the new task being learned. SPU strikes a balance between efficiency and new task performance, while maintaining the transferability and generalizability of foundation models. The authors first analyze the foundation model to identify the specific layers and parameters that are most relevant to the new task. They then propose a gradient-based scoring function to select a sparse set of parameters to update, while keeping the rest of the model frozen. Extensive evaluations on six continual learning tasks show that SPU can significantly improve the performance on new tasks (up to 7%) while preserving the pre-training knowledge with a negligible decrease of 0.9% on a representative control set accuracy. The authors also conduct in-depth analyses to understand the impact of each component on generic knowledge forgetting.

Stats

Updating merely 3% of the parameters can achieve superior performance on new tasks compared to fully finetuning the model. The proposed method preserves the pre-training knowledge with only a 0.9% drop in control set accuracy.

Quotes

"We propose a novel approach that, instead of updating all parameters equally, localizes the updates to a sparse set of parameters relevant to the task being learned." "Our method achieves improvements on the accuracy of the newly learned tasks up to 7% while preserving the pretraining knowledge with a negligible decrease of 0.9% on a representative control set accuracy."

Key Insights Distilled From

Overcoming Generic Knowledge Loss with Selective Parameter Update

by Wenxuan Zhan... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2308.12462.pdf

Deeper Inquiries

How can the proposed selective parameter update approach be extended to other types of foundation models beyond vision-language models

The proposed selective parameter update approach can be extended to other types of foundation models beyond vision-language models by adapting the localization and parameter selection strategies to suit the specific architecture and requirements of the new model. For instance, in natural language processing models, such as GPT (Generative Pre-trained Transformer) models, the localization of updates could focus on specific layers or components that are crucial for language understanding and generation. The parameter selection process could be tailored to identify key parameters related to language tasks, such as word embeddings or attention mechanisms. Similarly, for reinforcement learning models, the selective parameter update approach could target parameters associated with reward prediction, policy optimization, or value estimation. By localizing updates to relevant components like the policy network or value function, the model can adapt to new tasks while retaining its foundational knowledge. The parameter selection process could prioritize parameters that influence decision-making and learning in the reinforcement learning setting. In summary, the selective parameter update approach can be extended to various types of foundation models by customizing the localization and parameter selection strategies to align with the unique characteristics and requirements of each model architecture and domain.

What are the potential limitations of the gradient-based parameter selection strategy, and how could it be further improved

The gradient-based parameter selection strategy, while effective in identifying relevant parameters for task-specific updates, may have some limitations that could be further improved. One potential limitation is the sensitivity of the scoring function to noise in the gradients, which could lead to suboptimal parameter selection. To address this, techniques such as gradient smoothing or regularization could be employed to stabilize the scoring function and reduce the impact of noisy gradients. Another limitation is the potential bias in the gradient-based scoring function towards parameters with high gradients, which may not always correspond to the most relevant parameters for the task. To mitigate this bias, ensemble methods or alternative scoring functions that consider a combination of gradient magnitudes, parameter importance, and task-specific relevance could be explored. Furthermore, the gradient-based approach may struggle with capturing complex relationships between parameters and task performance in highly non-linear models. Incorporating higher-order derivatives or exploring alternative optimization techniques, such as evolutionary algorithms or reinforcement learning-based approaches, could enhance the parameter selection process and improve the overall performance of the selective parameter update strategy.

What are the implications of the findings in this work for the broader challenge of continual learning in AI systems that need to continuously expand their knowledge over time

The findings in this work have significant implications for the broader challenge of continual learning in AI systems that need to continuously expand their knowledge over time. By demonstrating the effectiveness of the selective parameter update approach in preserving pre-trained knowledge while adapting to new tasks, the study highlights a promising method for mitigating catastrophic forgetting and enabling lifelong learning in AI systems. One key implication is the potential for more efficient and effective continual learning algorithms that can adapt to new tasks without sacrificing previously acquired knowledge. This can lead to more robust and versatile AI systems that can continually improve and expand their capabilities over time. Additionally, the findings underscore the importance of balancing task-specific updates with the preservation of generic knowledge in continual learning scenarios. By localizing updates to specific parameters and layers relevant to the new task, AI systems can achieve better performance on new tasks while maintaining their transferability and generalizability across a wide range of domains. Overall, the insights from this work contribute to the advancement of continual learning techniques and pave the way for the development of more adaptive and intelligent AI systems that can continuously learn and evolve in dynamic environments.