Recent research on compressing deep neural networks has focused on reducing the number of parameters. Smaller networks are easier to export and deploy on edge-devices. We introduce Adjoined networks as a training approach that can compress and regularize any CNN-based neural architecture. Our one-shot learning paradigm trains both the original and the smaller networks together. The parameters of the smaller network are shared across both the architectures. For resnet-50 trained on Imagenet, we are able to achieve a $13.7x$ reduction in the number of parameters and a $3x$ improvement in inference time without any significant drop in accuracy. For the same architecture on CIFAR-100, we are able to achieve a $99.7x$ reduction in the number of parameters and a $5x$ improvement in inference time. On both these datasets, the original network trained in the adjoint fashion gains about $3\%$ in top-1 accuracy as compared to the same network trained in the standard fashion.

介绍了Adjoined Networks，一种新的神经网络压缩方法，采用同时训练基础网络和压缩网络的策略，通过压缩并规范化模型，使得模型在保持精度的同时，也具有较小的存储和计算需求。此外，提出了Differentiable Adjoined Networks，利用神经网络搜索技术，进一步提升了模型的压缩效果。

相邻网络：一种适用于网络压缩的训练范式