• There are considerable similarities with another paper [1] (see below references). The work in this paper is not novel and there are no citation given to [1]. EM approach, regeneration approach is mentioned in [1]. • The experimental results are not convincing. The paper provides that joint learning on CIFAR- 100 dataset gives 39.97% accuracy when tested on class incremental learning. However, there seems to be more accurate results obtained with CIFAR-100 dataset on class incremental learning. For example, the paper [2] obtains 58.4% accuracy. In addition, the memory size is 10 times lower than this setup. The experiments do not contain the paper [2]. Other relevant papers [3, 4] whose accuracies are listed higher for this dataset are not compared and referenced either. • Although it is provided that a 6-fold cross-validation is used for every dataset, the reason for cross-validation is not understood because other papers that this work compares to did not use the cross- validation in their papers. Therefore, it is not clear why 6-fold cross-validation is required for this problem. • The notation for results is not clear. The paper claims the improvement for CIFAR-10 is 3%p but it is not clear what %p stands for. • Although there is a reference to [2] and other types of rehearsal based continual learning methods, the experiments do not contain any of the rehearsal methods. • The setup for the experiments is missing. The code is not provided. • The effect of memory size is ambiguous. An ablation study containing the effect of memory size should be added for justifying the memory size selection. • In Table-1, the experimental results for CelebA dataset are written in caption. However, there are not any experiments with CelebA dataset. [1] Overcoming Catastrophic Forgetting with Gaussian Mixture Replay (Pfülb and Geppert, 2021) [2] Gdumb: A simple approach that questions our progress in continual learning (Prabhu et al., 2020) [3] Supervised Contrastive Learning (Khosla et al., 2020) [4] Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network (Kim and Choi, 2021)
• Although it is provided that a 6-fold cross-validation is used for every dataset, the reason for cross-validation is not understood because other papers that this work compares to did not use the cross- validation in their papers. Therefore, it is not clear why 6-fold cross-validation is required for this problem.