Consistent Prompting for Rehearsal-Free Continual Learning

• URL: http://arxiv.org/abs/2403.08568v2
• Date: Thu, 14 Mar 2024 12:26:17 GMT
• Title: Consistent Prompting for Rehearsal-Free Continual Learning
• Authors: Zhanxin Gao, Jun Cen, Xiaobin Chang,
• Abstract summary: Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Existing prompt-based methods are inconsistent between training and testing, limiting their effectiveness. We propose a novel prompt-based method, Consistent Prompting (CPrompt), for more aligned training and testing.
• Score: 5.166083532861163
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt-based methods are inconsistent between training and testing, limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment, leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper, we propose a novel prompt-based method, Consistent Prompting (CPrompt), for more aligned training and testing. Specifically, all existing classifiers are exposed to prompt training, resulting in classifier consistency learning. In addition, prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing.

Related papers

• Vector Quantization Prompting for Continual Learning [23.26682439914273]
  Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks. Recent top-performing approaches are prompt-based methods that utilize a set of learnable parameters to encode task knowledge. We propose VQ-Prompt, a prompt-based continual learning method that incorporates Vector Quantization into end-to-end training of a set of discrete prompts.
  arXiv  Detail & Related papers  (2024-10-27T13:43:53Z)
• Historical Test-time Prompt Tuning for Vision Foundation Models [99.96912440427192]
  HisTPT is a Historical Test-time Prompt Tuning technique that memorizes the useful knowledge of the learnt test samples. HisTPT achieves superior prompt tuning performance consistently while handling different visual recognition tasks.
  arXiv  Detail & Related papers  (2024-10-27T06:03:15Z)
• Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning [50.26965628047682]
  Adapting pre-trained models to open classes is a challenging problem in machine learning. In this paper, we consider combining the advantages of both and come up with a test-time prompt tuning approach. Our proposed method outperforms all comparison methods on average considering both base and new classes.
  arXiv  Detail & Related papers  (2024-08-29T12:34:01Z)
• Semantic Prompting with Image-Token for Continual Learning [7.5140668729696145]
  I-Prompt is a task-agnostic approach to eliminate task prediction. Our method achieves competitive performance on four benchmarks. We demonstrate the superiority of our method across various scenarios through extensive experiments.
  arXiv  Detail & Related papers  (2024-03-18T07:43:14Z)
• Any-Shift Prompting for Generalization over Distributions [66.29237565901734]
  We propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. Within this framework, the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. The network generates the tailored test prompt with both training and test information in a feedforward pass, avoiding extra training costs at test time.
  arXiv  Detail & Related papers  (2024-02-15T16:53:42Z)
• OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning [10.299813904573695]
  We propose a regularization method based on virtual outliers to tighten decision boundaries of the classifier. A simplified prompt-based method can achieve results comparable to previous state-of-the-art (SOTA) methods equipped with a prompt pool.
  arXiv  Detail & Related papers  (2024-02-06T16:31:11Z)
• Continuous Prompt Generation from Linear Combination of Discrete Prompt Embeddings [0.0]
  We present a novel method of constructing continuous prompts via discrete prompt embeddings and evaluate improvements to continuous prompt interpretability and inference accuracy. For a set of manually designed discrete prompts $mathcalD$, which we tokenize and embed each into tensor form, we train a model to predict the weights such that the linear combinations of those prompts correspond to higher performance on natural language understanding tasks.
  arXiv  Detail & Related papers  (2023-12-16T05:02:06Z)
• Understanding prompt engineering may not require rethinking generalization [56.38207873589642]
  We show that the discrete nature of prompts, combined with a PAC-Bayes prior given by a language model, results in generalization bounds that are remarkably tight by the standards of the literature. This work provides a possible justification for the widespread practice of prompt engineering.
  arXiv  Detail & Related papers  (2023-10-06T00:52:48Z)
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting [112.66832145320434]
  We introduce a self-regularization framework for prompting called PromptSRC. PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations.
  arXiv  Detail & Related papers  (2023-07-13T17:59:35Z)
• TEMPERA: Test-Time Prompting via Reinforcement Learning [57.48657629588436]
  We propose Test-time Prompt Editing using Reinforcement learning (TEMPERA) In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
  arXiv  Detail & Related papers  (2022-11-21T22:38:20Z)
• Continued Pretraining for Better Zero- and Few-Shot Promptability [44.381944544918014]
  We show that a simple recipe, continued pretraining that incorporates a trainable prompt during multi-task learning, leads to improved promptability in both zero- and few-shot settings. On the other hand, continued pretraining using MAML-style meta-learning, a method that directly optimize few-shot promptability, yields subpar performance.
  arXiv  Detail & Related papers  (2022-10-19T02:41:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.