# Towards Efficient Microarchitectural Design for Accelerating Unsupervised GAN-Based Deep Learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/towards-efficient-microarchitectural-design-for-accelerating-unsupervised-gan/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Mingcong Song,Jiaqi Zhang,Huixiang Chen,Tao Li |
| Citations | 61 |
| DOI | 10.1109/hpca.2018.00016 |
| Fields | Computer Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2794986474 |
| Type | conference-paper |
| Year | 2018 |

## Paper authors

- [Huixiang Chen](https://scholariq.org/researchers/huixiang-chen/)

## Paper primary topic

- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

## Paper topics

- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)
- [Adversarial Robustness in Machine Learning](https://scholariq.org/topics/adversarial-robustness-in-machine-learning/)
- [Generative Adversarial Networks and Image Synthesis](https://scholariq.org/topics/generative-adversarial-networks-and-image-synthesis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.