insight - Computer vision, machine learning - # Continual learning with pre-trained vision transformers

Automated Expansion of Pre-trained Vision Transformers for Continual Learning

Q: How can the proposed self-expansion mechanism be extended to other types of pre-trained models beyond vision transformers

The proposed self-expansion mechanism can be extended to other types of pre-trained models beyond vision transformers by adapting the modularized adaptation framework to suit the specific architecture and characteristics of the new models. For instance, in the case of language models such as BERT or GPT, the self-expansion approach can be implemented by adding adapters or modules at different layers of the transformer architecture. The representation descriptors can be tailored to capture the distributional shifts in text data, enabling the model to dynamically expand and adapt to new tasks in a continual learning setting. By customizing the expansion strategy to the unique features of different pre-trained models, the self-expansion mechanism can be effectively applied across a variety of domains and tasks.

Q: What are the potential limitations of the current approach, and how can it be further improved to handle more complex continual learning scenarios

One potential limitation of the current approach is the restriction to expansion at most once per layer for each task, which may limit the flexibility of model expansion in more complex continual learning scenarios. To address this limitation and further improve the approach, several enhancements can be considered: Dynamic Expansion Thresholds: Introduce adaptive expansion thresholds that can vary based on the complexity of the incoming tasks or the level of distribution shift detected by the representation descriptors. Adaptive Expansion Strategies: Implement adaptive expansion strategies that consider the interplay between different layers of the model and dynamically adjust the expansion process based on the task requirements. Hierarchical Expansion: Explore hierarchical expansion mechanisms that allow for multi-level adaptation, enabling the model to expand at different levels of abstraction based on the task complexity. Selective Expansion: Develop selective expansion techniques that prioritize the addition of adapters based on the relevance and importance of the new tasks, optimizing the model's adaptation capacity. By incorporating these enhancements, the self-expansion mechanism can be further refined to handle more complex continual learning scenarios with improved adaptability and efficiency.

Q: What are the broader implications of developing continual learning methods that can efficiently adapt pre-trained models to new tasks without catastrophic forgetting, and how might this impact the development of more robust and versatile AI systems

The development of continual learning methods that efficiently adapt pre-trained models to new tasks without catastrophic forgetting has significant implications for the advancement of AI systems. Some of the broader implications include: Enhanced Model Flexibility: By enabling pre-trained models to dynamically expand and adapt to new tasks, AI systems can become more flexible and versatile, allowing for seamless integration of new knowledge without compromising existing learning. Improved Task Generalization: Continual learning methods that prevent catastrophic forgetting facilitate better task generalization, enabling AI systems to perform effectively across a wide range of tasks and domains. Efficient Knowledge Transfer: The ability to efficiently adapt pre-trained models to new tasks enhances knowledge transfer capabilities, allowing AI systems to leverage previously learned knowledge effectively in diverse scenarios. Robust AI Systems: Developing robust and versatile AI systems that can continually learn and adapt without forgetting paves the way for the creation of more resilient and adaptive intelligent systems that can evolve over time. Overall, the development of efficient continual learning methods has the potential to drive advancements in AI research and applications, leading to the creation of more robust and adaptive AI systems with improved performance and versatility.

Core Concepts

A novel continual learning approach that automatically expands pre-trained vision transformers by adding modular adapters and representation descriptors to accommodate distribution shifts in incoming tasks, without the need for memory rehearsal.

Abstract

The paper proposes a continual learning framework called SEMA (Self-Expansion of pre-trained Models with Modularized Adaptation) that can be integrated into transformer-based pre-trained models like Vision Transformer (ViT).

Key highlights:

SEMA employs a modular adapter design, where each adapter module consists of a functional adapter and a representation descriptor. The representation descriptor captures the distribution of the relevant input features and serves as an indicator of distribution shift during training.
SEMA automatically decides whether to reuse existing adapters or add new ones based on the distribution shift detected by the representation descriptors. This allows the model to efficiently accommodate changes in the incoming data distribution without overwriting previously learned knowledge.
An expandable weighting router is learned jointly with the adapter modules to effectively combine the outputs of different adapters.
SEMA operates without the need for memory rehearsal and outperforms state-of-the-art ViT-based continual learning methods on various benchmarks, including CIFAR-100, ImageNet-R, ImageNet-A, and VTAB.

The paper also provides extensive ablation studies and analyses to validate the effectiveness of the proposed self-expansion mechanism and the design choices.

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Stats

"We demonstrate that the proposed framework outperforms the state-of-the-art without memory rehearsal."
"By comparing with vision-transformer-based continual learning adaptation methods, we demonstrate that the proposed framework outperforms the state-of-the-art without memory rehearsal."

Quotes

"To address the difficulty of maintaining all seen data and repeatedly re-training the model, continual learning (CL) aims to learn incrementally from a continuous data stream [15, 52, 62]."
"While most CL methods [6,42,70] root in the "training-from-scratch" paradigm, recent works have started to explore the potential of integrating pre-trained foundation models into CL as robust feature extractors [45, 79], or adapting them to downstream tasks through parameter-efficient fine-tuning with prompts and/or adapters [14, 60, 65, 66, 79, 80]."

Key Insights Distilled From

Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning

by Huiyi Wang,H... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.18886.pdf

Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning

Deeper Inquiries

How can the proposed self-expansion mechanism be extended to other types of pre-trained models beyond vision transformers

The proposed self-expansion mechanism can be extended to other types of pre-trained models beyond vision transformers by adapting the modularized adaptation framework to suit the specific architecture and characteristics of the new models. For instance, in the case of language models such as BERT or GPT, the self-expansion approach can be implemented by adding adapters or modules at different layers of the transformer architecture. The representation descriptors can be tailored to capture the distributional shifts in text data, enabling the model to dynamically expand and adapt to new tasks in a continual learning setting. By customizing the expansion strategy to the unique features of different pre-trained models, the self-expansion mechanism can be effectively applied across a variety of domains and tasks.

What are the potential limitations of the current approach, and how can it be further improved to handle more complex continual learning scenarios

One potential limitation of the current approach is the restriction to expansion at most once per layer for each task, which may limit the flexibility of model expansion in more complex continual learning scenarios. To address this limitation and further improve the approach, several enhancements can be considered:

Dynamic Expansion Thresholds: Introduce adaptive expansion thresholds that can vary based on the complexity of the incoming tasks or the level of distribution shift detected by the representation descriptors.
Adaptive Expansion Strategies: Implement adaptive expansion strategies that consider the interplay between different layers of the model and dynamically adjust the expansion process based on the task requirements.
Hierarchical Expansion: Explore hierarchical expansion mechanisms that allow for multi-level adaptation, enabling the model to expand at different levels of abstraction based on the task complexity.
Selective Expansion: Develop selective expansion techniques that prioritize the addition of adapters based on the relevance and importance of the new tasks, optimizing the model's adaptation capacity.

By incorporating these enhancements, the self-expansion mechanism can be further refined to handle more complex continual learning scenarios with improved adaptability and efficiency.

What are the broader implications of developing continual learning methods that can efficiently adapt pre-trained models to new tasks without catastrophic forgetting, and how might this impact the development of more robust and versatile AI systems

The development of continual learning methods that efficiently adapt pre-trained models to new tasks without catastrophic forgetting has significant implications for the advancement of AI systems. Some of the broader implications include:

Enhanced Model Flexibility: By enabling pre-trained models to dynamically expand and adapt to new tasks, AI systems can become more flexible and versatile, allowing for seamless integration of new knowledge without compromising existing learning.
Improved Task Generalization: Continual learning methods that prevent catastrophic forgetting facilitate better task generalization, enabling AI systems to perform effectively across a wide range of tasks and domains.
Efficient Knowledge Transfer: The ability to efficiently adapt pre-trained models to new tasks enhances knowledge transfer capabilities, allowing AI systems to leverage previously learned knowledge effectively in diverse scenarios.
Robust AI Systems: Developing robust and versatile AI systems that can continually learn and adapt without forgetting paves the way for the creation of more resilient and adaptive intelligent systems that can evolve over time.

Overall, the development of efficient continual learning methods has the potential to drive advancements in AI research and applications, leading to the creation of more robust and adaptive AI systems with improved performance and versatility.