OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning

Wei-Cheng Huang, Chun-Fu Chen, Hsiang Hsu

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Keywords: Rehearsal-free continual learning, Class-incremental learning, Parameter-efficient fine-tuning, Outlier regularization

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Abstract:

Recent works have shown that by using large pre-trained models along with learnable prompts, rehearsal-free methods
for class-incremental learning (CIL) settings can achieve superior performance to prominent rehearsal-based ones.
Rehearsal-free CIL methods struggle with distinguishing classes from different tasks, as those are not trained together.
In this work we propose a regularization method based on virtual outliers to tighten decision boundaries of the classifier,
such that confusion of classes among different tasks is mitigated.
Recent prompt-based methods often require a pool of task-specific prompts, in order to prevent overwriting knowledge
of previous tasks with that of the new task, leading to extra computation in querying and composing an
appropriate prompt from the pool.
This additional cost can be eliminated, without sacrificing accuracy, as we reveal in the paper.
We illustrate that a simplified prompt-based method can achieve results comparable to
previous state-of-the-art (SOTA) methods equipped with a prompt pool, using much less learnable parameters and lower inference cost.
Our regularization method has demonstrated its compatibility with different prompt-based methods, boosting
those previous SOTA rehearsal-free CIL methods' accuracy on the ImageNet-R and CIFAR-100 benchmarks. Our source code is available at https://github.com/jpmorganchase/ovor.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Primary Area: transfer learning, meta learning, and lifelong learning

Submission Number: 6042