Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

CVPR 2018 Jason KuenXiangfei KongZhe LinGang WangJianxiong YinSimon SeeYap-Peng Tan

It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference cannot be known beforehand during training, or the inference budget is dependent on the changing real-time resource availability... (read more)

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