Semantic Residual Prompts for Continual Learning: A Novel Approach to Enhance Stability and Plasticity in CL

핵심 개념

The author proposes STAR-Prompt, a two-level prompting strategy that leverages a foundation model to enhance stability and plasticity in Continual Learning. By introducing semantic residuals and generative replay, the method outperforms existing approaches.

초록

Semantic Residual Prompts for Continual Learning introduces STAR-Prompt, a novel approach that enhances prompt selection stability and plasticity in CL. The method utilizes CLIP models to improve prompt selection strategies and introduces semantic residuals for better adaptation. Extensive experiments demonstrate significant performance improvements over state-of-the-art methods across various datasets.
Key points:

Prompt-tuning methods freeze large pre-trained models and focus on prompts.
Existing approaches face catastrophic forgetting due to unstable prompt selection.
STAR-Prompt uses CLIP to stabilize prompt selection with two-level adaptation.
Semantic residuals are introduced to transfer semantics to ViT layers.
Generative replay with MoGs enhances adaptability across different domains.
Extensive experiments show superior performance over existing methods.

통계

Through extensive analysis on established CL benchmarks, we show that our method significantly outperforms both state-of-the-art CL approaches and the zero-shot CLIP test.
Notably, our findings hold true even for datasets with a substantial domain gap w.r.t. the pre-training knowledge of the backbone model.

인용구

"Our results indicate that STAR-Prompt significantly outperforms existing approaches in terms of stability and adaptability."
"Our method introduces a novel residual mechanism to transfer CLIP semantics to ViT layers."

핵심 통찰 요약

Semantic Residual Prompts for Continual Learning

by Martin Menab... 게시일 arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06870.pdf

더 깊은 질문

How can the concept of semantic residuals be applied in other machine learning tasks

The concept of semantic residuals can be applied in various machine learning tasks to enhance model adaptability and performance. In natural language processing tasks, semantic residuals can be used to transfer contextual information from pre-trained language models like BERT or GPT to downstream models, improving their understanding of text semantics. In computer vision tasks, semantic residuals can help transfer visual cues learned by a foundation model like ResNet or EfficientNet to fine-tuned models for specific image classification tasks. Additionally, in reinforcement learning, semantic residuals could aid in transferring knowledge about optimal actions or state representations from one task to another, facilitating faster learning and improved performance.

What are the potential limitations or drawbacks of relying on foundation models like CLIP for prompt selection

Relying on foundation models like CLIP for prompt selection may have some limitations and drawbacks. One potential limitation is the risk of overfitting to the specific characteristics of the foundation model's embeddings or representations. This could lead to biases in prompt selection that are not generalizable across different datasets or domains. Another drawback is the computational cost associated with using large-scale foundation models for prompt selection, which may limit scalability and efficiency in real-world applications. Additionally, there may be challenges in interpreting and explaining the decisions made by a model that relies heavily on a complex foundation model for prompt selection.

How might generative replay techniques impact long-term memory retention in continual learning scenarios

Generative replay techniques can have a significant impact on long-term memory retention in continual learning scenarios by mitigating catastrophic forgetting and enabling the preservation of knowledge learned from past experiences. By generating synthetic samples based on previously seen data distributions through generative replay methods like Mixture of Gaussians (MoGs), models can continuously train on both new and old data without experiencing significant degradation in performance on earlier tasks. This approach helps maintain a balance between stability (retaining knowledge) and plasticity (learning new information) throughout continual learning processes, ultimately improving overall memory retention capabilities over time.