Appendix RepresentationLearningProcess

Here we provide more experimental results. Specifically, we evaluate the representational similarity using the CKA value of the same layer from the model (ResNet-32) with different sparsity at each epoch and compare them with the final model. In our work, we evaluate four different types of freezing schemes (Sec. Inthis case, we can keep the single-shot & resume has the same FLOPs reduction asthe single-shot scheme, and the entire network can be fine-tuned at the end of training with a small learningrate. For the periodically freezing scheme, we let the selected layers freeze periodically with a given frequency so that all the layers/blocks are able to be updated at different stages of the training process.