Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure
Hamed Hakkak
文章地址
摘要
基于模型的压缩是一种有效的、方便的、扩展的神经网络模型模型，它需要的计算能力和功耗都是有限的。然而，传统的压缩技术模型使用了精心设计的特性，并探索了在大小、速度和精度方面的大型空间的探索和设计的专门领域，这些领域通常的回报更少，时间也在增加。本文将会通过采样和空间设计分析强化学习在深度自动压缩，同时提压缩模型的质量。在没有任何人工操作的情况下，以完全自动化的方式获得了先进模型的压缩结果。在浮点运算缩减为\frac{1}{4}的情况下，实现了2.8%的精度，高于ImageNet中VGG-16的手动压缩模型。
Abstract
Model-based compression is an effective, facilitating, and expanded model of neural network models with limited computing and low power. However, conventional models of compression techniques utilize crafted features and explore specialized areas for exploration and design of large spaces in terms of size, speed, and accuracy, which usually have returns Less and time is up. This paper will effectively analyze deep auto compression (ADC) and reinforcement learning strength in an effective sample and space design, and improve the compression quality of the model. The results of compression of the advanced model are obtained without any human effort and in a completely automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher than the manual compression model for VGG-16 in ImageNet.
