A key challenge for machine intelligence is to learn new visual concepts without forgetting the previously acquired knowledge. Continual learning is aimed towards addressing this challenge. However, there is a gap between existing supervised continual learning and human-like intelligence, where human is able to learn from both labeled and unlabeled data. How unlabeled data affects learning and catastrophic forgetting in the continual learning process remains unknown. To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models. Specifically, a novel gradient learner learns from labeled data to predict gradients on unlabeled data. Hence, the unlabeled data could fit into the supervised continual learning method. Different from conventional semi-supervised settings, we do not hypothesize that the underlying classes, which are associated to the unlabeled data, are known to the learning process. In other words, the unlabeled data could be very distinct from the labeled data. We evaluate the proposed method on mainstream continual learning, adversarial continual learning, and semi-supervised learning tasks. The proposed method achieves state-of-the-art performance on classification accuracy and backward transfer in the continual learning setting while achieving desired performance on classification accuracy in the semi-supervised learning setting. This implies that the unlabeled images can enhance the generalizability of continual learning models on the predictive ability on unseen data and significantly alleviate catastrophic forgetting. The code is available at \url{https://github.com/luoyan407/grad_prediction.git}.

翻译：机器智能的核心挑战之一是在不遗忘已有知识的前提下学习新的视觉概念。持续学习旨在解决这一挑战，但现有监督式持续学习与人类智能之间存在显著差距——人类能够同时从标记与未标记数据中学习。在持续学习过程中，未标记数据如何影响学习效果及灾难性遗忘尚不明确。为探究这些问题，我们提出了一种可泛化应用于现有持续学习模型的新型半监督持续学习方法。具体而言，通过设计新颖的梯度学习器，从标记数据中学习预测未标记数据的梯度，从而将未标记数据融入监督式持续学习框架。与常规半监督设置不同，我们不做未标记数据对应底层类别已知的假设，即未标记数据可能与标记数据存在显著差异。我们在主流持续学习、对抗性持续学习及半监督学习任务上评估了所提方法。实验表明，该方法在持续学习场景下取得了分类准确率与反向迁移指标的领先性能，同时在半监督学习场景下保持了理想的分类准确率。这表明未标记图像能增强持续学习模型对未知数据的预测泛化能力，并显著缓解灾难性遗忘。代码已开源至 \url{https://github.com/luoyan407/grad_prediction.git}。