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:机器智能的关键挑战是如何学习新的视觉概念而不忘记先前获得的知识。 持续学习是旨在应对这一挑战。 但是, 现有的受监管的不断学习和人类智能之间存在差距, 人类能够从标签和未标签数据中学习。 没有标签的数据如何影响学习和持续学习过程中的灾难性遗忘, 仍然未知。 要探索这些问题, 我们制定一个新的半监督的连续学习方法, 可以通用地应用到现有的持续学习模式。 具体地说, 新的梯度学习者从标签数据中学习, 以预测未标签数据上的梯度。 因此, 无标签的数据可以适合受监管的持续学习方法。 不同于传统的半监督的设置, 我们并不低估与未标签数据相关的基本课程是如何影响学习的。 换句话说, 未标签的数据可能与标签数据非常不同。 我们评估了关于主流持续学习、 对抗性持续学习、 半监督学习任务的拟议方法。 拟议的方法在常规的 Obliver 分类中, 在不断学习的精确度上, 在持续学习的精确度上, 在持续学习的精确度上, 能够大幅地学习 学习 继续学习 持续学习的精确性 。