PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
- URL: http://arxiv.org/abs/2309.07117v1
- Date: Wed, 13 Sep 2023 17:55:11 GMT
- Title: PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
- Authors: Hai-Long Sun, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
- Abstract summary: This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.
On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.
On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
- Score: 71.63186089279218
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While traditional machine learning can effectively tackle a wide range of
problems, it primarily operates within a closed-world setting, which presents
limitations when dealing with streaming data. As a solution, incremental
learning emerges to address real-world scenarios involving new data's arrival.
Recently, pre-training has made significant advancements and garnered the
attention of numerous researchers. The strong performance of these pre-trained
models (PTMs) presents a promising avenue for developing continual learning
algorithms that can effectively adapt to real-world scenarios. Consequently,
exploring the utilization of PTMs in incremental learning has become essential.
This paper introduces a pre-trained model-based continual learning toolbox
known as PILOT. On the one hand, PILOT implements some state-of-the-art
class-incremental learning algorithms based on pre-trained models, such as L2P,
DualPrompt, and CODA-Prompt. On the other hand, PILOT also fits typical
class-incremental learning algorithms (e.g., DER, FOSTER, and MEMO) within the
context of pre-trained models to evaluate their effectiveness.
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