the empirical validation is weak. Therefore, more new models need to be compared. For more details, please refer to “Reasons for reject”
Reasons for accept: 1. The structure of this paper is clear and easy to read. Specifically, the motivation of this paper is clear and the structure is well organized; the related work is elaborated in detail; the experimental setup is complete. 2. Based on the use of replay to solve catastrophic forgetting, the current popular graph structure is introduced to capture the similarities between samples. Combined with the proposed Graph Regularization, this paper provides a new perspective for solving catastrophic forgetting. 3. The experimental results given in the paper can basically show that the proposed method is effective. The ablation study also verified the effectiveness of each component.
Reasons for reject: 1. The lack of comparison of experimental effects after replacing Graph Regularization with other regularization methods mentioned in this paper, or other distance measurement methods, eg., L2.
This paper compares relatively few baselines, especially recent studies. I hope to see the comparison results of some papers in the list below. The latest papers on the three types of methods (regularization, expansion, and rehearsal) for solving catastrophic forgetting are included. Therefore, if it can be compared with some of these models, it will be beneficial to the evaluation of GCL.
[1] Ostapenko O , Puscas M , Klein T , et al. Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning. ICML 2019 [2] Y Wu, Y Chen, et al. Large Scale Incremental Learning. CVPR 2019 [3] Liu Y , Liu A A , Su Y , et al. Mnemonics training: Multi-class incremental learning without forgetting. CVPR 2020 [4] Zhang J , Zhang J , Ghosh S , et al. Class-incremental learning via deep model consolidation. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) [5] Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu. Continuous learning of context-dependent processing in neural networks. Nature Machine Intelligence, 2019. [6] Wenpeng Hu, Zhou Lin, et al. Overcoming catastrophic forgetting for continual learning via model adaptation. ICLR 2019 [7] Rao D , Visin F , Rusu A A , et al. Continual Unsupervised Representation Learning. NeurIPS 2019
1. The structure of this paper is clear and easy to read. Specifically, the motivation of this paper is clear and the structure is well organized; the related work is elaborated in detail; the experimental setup is complete.