1. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow IJ, Harp A, Irving G, Isard M, Jia Y, Józefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray DG, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker PA, Vanhoucke V, Vasudevan V, Viégas FB, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467. arXiv:1603.04467
2. Chen Z, Chen Z, Lin J, Liu S, Li W (2020) Deep neural network acceleration based on low-rank approximated channel pruning. IEEE Trans Circuits Syst I Regul Pap 67(4):1232–1244
3. Chen Z, Xu TB, Du C, Liu CL, He H (2020) Dynamical channel pruning by conditional accuracy change for deep neural networks. IEEE Trans Neural Netw Learn Syst
4. Chollet F (2016) Xception: Deep learning with depthwise separable convolutions. CoRR abs/1610.02357. arXiv:1610.02357
5. Chrabaszcz P, Loshchilov I, Hutter F (2017) A downsampled variant of imagenet as an alternative to the CIFAR datasets. CoRR abs/1707.08819. arXiv:1707.08819