Semantically-Shifted Incremental Adapter-Tuning: A Continual Learning Framework for ViTransformers

Core Concepts

The core message of this paper is that incrementally tuning the shared adapter without imposing parameter update constraints is an effective continual learning strategy for pre-trained vision transformers, and further performance improvements can be achieved by retraining a unified classifier with semantic shift-compensated prototypes.

Abstract

The paper proposes a continual learning framework for pre-trained vision transformers (ViTransformers) that addresses the challenge of class-incremental learning (CIL). The key insights are:

Incremental adapter tuning: The authors observe that incrementally tuning the shared adapter is superior to other parameter-efficient tuning (PET) methods like prompt-tuning and SSF-tuning in the CIL setting. Adapter tuning effectively balances learning new classes and maintaining performance on old classes, without the need to construct an adapter pool.

Unconstrained adapter tuning: The authors analyze that constraining parameter updates to mitigate forgetting can hinder the plasticity required for learning new classes, due to the similarity in parameter sensitivity across tasks. Therefore, they propose to incrementally tune the shared adapter without any parameter update constraints.

Semantic shift estimation and unified classifier retraining: Since the feature distribution of old classes changes over time due to incremental training, the authors propose to estimate the semantic shift of old prototypes without access to past samples. They then use the updated prototypes to retrain a unified classifier, further improving performance.

The proposed method, called SSIAT, eliminates the need for model expansion and retaining any image samples. Extensive experiments on five CIL benchmarks demonstrate the superiority of SSIAT, achieving state-of-the-art performance.

Stats

The average accuracy of the last incremental session on ImageNetR is 79.38%.
The average accuracy across all incremental sessions on ImageNetR is 83.63%.
The average accuracy of the last incremental session on ImageNetA is 62.43%.
The average accuracy across all incremental sessions on ImageNetA is 70.83%.

Quotes

"Confining the change of parameters of previous tasks hinders the plasticity of new classes due to the similarity of parameter sensitivity among tasks."
"We estimate the semantic shift of old prototypes without access to past samples and update stored prototypes session by session."

Key Insights Distilled From

Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer

by Yuwen Tan,Qi... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19979.pdf

Deeper Inquiries

How can the proposed continual learning framework be extended to other types of pre-trained models beyond vision transformers

The proposed continual learning framework can be extended to other types of pre-trained models beyond vision transformers by adapting the tuning strategies to suit the specific architecture and characteristics of the model. For instance, for pre-trained language models like BERT or GPT, the adapter tuning approach can be modified to incorporate task-specific adapters for incremental learning tasks. The shared adapter concept can be applied by inserting adapter modules in key layers of the language model to facilitate learning new tasks while retaining knowledge from previous tasks. Additionally, the semantic shift estimation technique can be adapted to analyze the changes in word embeddings or contextual representations in language models across different learning sessions. By customizing the tuning mechanisms and semantic shift estimation process to align with the structure and functionality of different pre-trained models, the continual learning framework can be effectively applied to a variety of model architectures.

What are the potential limitations of the semantic shift estimation approach, and how can it be further improved

The semantic shift estimation approach, while effective in estimating the changes in old prototypes without access to past samples, may have limitations in scenarios where the feature distribution of old classes undergoes significant changes over time. One potential limitation is the accuracy of the estimated semantic shifts, especially in cases where the feature representations of old classes evolve in a non-linear or complex manner. To improve this approach, incorporating more advanced techniques such as domain adaptation methods or generative modeling to simulate the evolution of feature distributions over time could enhance the accuracy of the estimated semantic shifts. Additionally, exploring ensemble methods or incorporating uncertainty estimation techniques to account for the variability in the estimated shifts could provide more robust estimates. Furthermore, integrating feedback mechanisms or reinforcement learning algorithms to dynamically adjust the estimation process based on model performance could enhance the adaptability and accuracy of the semantic shift estimation approach.

Can the insights from this work be applied to develop continual learning strategies for multi-modal or cross-modal tasks

The insights from this work can be applied to develop continual learning strategies for multi-modal or cross-modal tasks by leveraging the shared adapter tuning approach and semantic shift estimation techniques across different modalities. For multi-modal tasks involving vision and language, the shared adapter concept can be extended to incorporate adapters for both visual and textual modalities, allowing the model to learn new tasks incrementally while preserving knowledge from previous tasks in a multi-modal setting. The semantic shift estimation approach can be adapted to analyze the changes in feature representations across different modalities and estimate the semantic shifts between tasks in a multi-modal learning scenario. By integrating the shared adapter tuning and semantic shift estimation techniques across modalities, a unified continual learning framework can be developed for multi-modal or cross-modal tasks, enabling models to learn continuously from diverse data streams while mitigating catastrophic forgetting and adapting to new tasks effectively.