Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via efficient model compression. Disentangled latent representations produced by variational autoencoder (VAE) networks are a promising approach for achieving model compression because they mainly retain task-specific information, discarding useless information for the task at hand. We make use of the Beta-VAE framework combined with a standard criterion for pruning to investigate the impact of forcing the network to learn disentangled representations on the pruning process for the task of classification. In particular, we perform experiments on MNIST and CIFAR10 datasets, examine disentanglement challenges, and propose a path forward for future works.

部署深度学习神经网络在边缘设备上，实现真实世界中特定目标，需要减少其内存占用、功耗和延迟。高效模型压缩可以实现这一目标。变分自编码器（VAE）网络产生的解缠缠绕的潜在表示是实现模型压缩的有希望方法，因为它们主要保留了与任务相关的信息，并丢弃了对当前任务无用的信息。我们利用Beta-VAE框架结合标准修剪准则，研究了迫使网络学习解缠缠绕表示对于分类任务的修剪过程的影响。具体来说，我们在MNIST和CIFAR10数据集上进行实验，研究了解缠缠绕的挑战，并为未来的研究提出了一个方向。

解缠对神经网络剪枝的影响