Continual Learning (CL) aims to incrementally update a trained model on new tasks without forgetting the acquired knowledge of old ones. Existing CL methods usually reduce forgetting with task priors, \ie using task identity or a subset of previously seen samples for model training. However, these methods would be infeasible when such priors are unknown in real-world applications. To address this fundamental but seldom-studied problem, we propose a Prior-Free Continual Learning (PFCL) method, which learns new tasks without knowing the task identity or any previous data. First, based on a fixed single-head architecture, we eliminate the need for task identity to select the task-specific output head. Second, we employ a regularization-based strategy for consistent predictions between the new and old models, avoiding revisiting previous samples. However, using this strategy alone often performs poorly in class-incremental scenarios, particularly for a long sequence of tasks. By analyzing the effectiveness and limitations of conventional regularization-based methods, we propose enhancing model consistency with an auxiliary unlabeled dataset additionally. Moreover, since some auxiliary data may degrade the performance, we further develop a reliable sample selection strategy to obtain consistent performance improvement. Extensive experiments on multiple image classification benchmark datasets show that our PFCL method significantly mitigates forgetting in all three learning scenarios. Furthermore, when compared to the most recent rehearsal-based methods that replay a limited number of previous samples, PFCL achieves competitive accuracy. Our code is available at: https://github.com/visiontao/pfcl

翻译：持续学习（CL）旨在逐步更新已训练模型，使其能够学习新任务，同时避免遗忘旧任务的已有知识。现有持续学习方法通常利用任务先验（即使用任务标识或之前见过的样本子集进行模型训练）来减少遗忘。然而，当这些先验在现实应用中未知时，此类方法将不可行。为解决这一基础但鲜有研究的问题，我们提出了一种无先验持续学习（PFCL）方法，该方法无需知道任务标识或任何先前数据即可学习新任务。首先，基于固定的单头架构，我们消除了为选择任务特定输出头而需要任务标识的需求。其次，我们采用基于正则化的策略来保持新旧模型之间预测的一致性，从而避免重新访问先前样本。然而，单独使用该策略在类增量场景中表现较差，尤其是在处理长任务序列时。通过分析传统基于正则化方法的有效性和局限性，我们提出额外利用辅助无标签数据集来增强模型一致性。此外，由于部分辅助数据可能降低性能，我们进一步开发了一种可靠的样本选择策略，以获得一致的性能提升。在多个图像分类基准数据集上的大量实验表明，我们的PFCL方法在所有三种学习场景下显著缓解了遗忘问题。此外，与最新基于复述的方法（即重放有限数量的先前样本）相比，PFCL实现了具有竞争力的准确率。我们的代码可在以下链接获取：https://github.com/visiontao/pfcl