# Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure

Source: [Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure](<https://www.sota2.com/research/paper/68fa64861e209131cea05273>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:06:03.000Z

Authors: Hamed Hakkak

Published: 2018-07-08T21:34:30.000Z

## 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 \[2,3,12\] 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.02886>)

[PDF](<https://arxiv.org/pdf/1807.02886.pdf>)