我们提出了一种无需使用任何数据即可压缩经过训练的神经网络的有效方法。我们的无数据方法所需的FLOP数量要比同类先进方法少14-450倍。..

Layer-Wise Data-Free CNN Compression

We present an efficient method for compressing a trained neural network without using any data. Our data-free method requires 14x-450x fewer FLOPs than comparable state-of-the-art methods.We break the problem of data-free network compression into a number of independent layer-wise compressions. We show how to efficiently generate layer-wise training data, and how to precondition the network to maintain accuracy during layer-wise compression. We show state-of-the-art performance on MobileNetV1 for data-free low-bit-width quantization. We also show state-of-the-art performance on data-free pruning of EfficientNet B0 when combining our method with end-to-end generative methods.