Motivated by the fact that local learning can limit memory when training the network and the adaptive nature of each individual block, the paper extends local learning to the ResNet-50 to handle large datasets. However, it seems that the results of the paper do not demonstrate the benefits of doing so. The detailed weaknesses are as follows: 1)The method proposed in the paper essentially differs very little from the traditional BP method. The main contribution of the paper is adding the stop gradient operation between blocks, which appears to be less innovative. 2)The local learning strategy is not superior to the BP optimization method. In addition, the model is more sensitive to each block after the model is blocked, especially the first block. More additional corrections are needed to improve the performance and robustness of the model, although still lower than BP's method. 3)Experimental results show that simultaneous blockwise training is better than sequential blockwise training. But the simultaneous blockwise training strategy cannot limit memory. 4)The blockwise training strategy relies on a special network structure like the block structure of the ResNet-50 model. 5)There are some writing errors in the paper, such as "informative informative" on page 5 and "performance" on page 1, which lacks a title.
5)There are some writing errors in the paper, such as "informative informative" on page 5 and "performance" on page 1, which lacks a title.