Continual learning (CL) adapt the deep learning scenarios with timely updated datasets. However, existing CL models suffer from the catastrophic forgetting issue, where new knowledge replaces past learning. In this paper, we propose Continual Learning with Task Specialists (CLTS) to address the issues of catastrophic forgetting and limited labelled data in real-world datasets by performing class incremental learning of the incoming stream of data. The model consists of Task Specialists (T S) and Task Predictor (T P ) with pre-trained Stable Diffusion (SD) module. Here, we introduce a new specialist to handle a new task sequence and each T S has three blocks; i) a variational autoencoder (V AE) to learn the task distribution in a low dimensional latent space, ii) a K-Means block to perform data clustering and iii) Bootstrapping Language-Image Pre-training (BLIP ) model to generate a small batch of captions from the input data. These captions are fed as input to the pre-trained stable diffusion model (SD) for the generation of task samples. The proposed model does not store any task samples for replay, instead uses generated samples from SD to train the T P module. A comparison study with four SOTA models conducted on three real-world datasets shows that the proposed model outperforms all the selected baselines

翻译：持续学习（CL）通过及时更新的数据集适应深度学习场景。然而，现有CL模型存在灾难性遗忘问题，即新知识覆盖过去所学。本文提出基于任务专家的持续学习（CLTS），通过对输入数据流进行类别增量学习，以解决现实数据集中灾难性遗忘和标注数据有限的问题。该模型由任务专家（TS）、任务预测器（TP）及预训练稳定扩散（SD）模块构成。我们引入新专家处理新任务序列，每个TS包含三个模块：i）变分自编码器（VAE），用于在低维潜在空间中学习任务分布；ii）K-Means模块，执行数据聚类；iii）自举语言-图像预训练（BLIP）模型，从输入数据生成小批量描述文本。这些描述文本输入预训练稳定扩散模型（SD）以生成任务样本。所提模型不存储任何任务样本进行回放，而是利用SD生成的样本训练TP模块。在三个真实数据集上与四种SOTA模型的对比研究表明，所提模型性能优于所有基线方法。