# Generative Semantic Manipulation with Contrasting GAN

arXiv: https://arxiv.org/abs/1708.00315 · HTML: https://mlsanity.com/paper/1708.00315/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation](https://mlsanity.com/paper/2102.12593/) (2021)
- [Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator](https://mlsanity.com/paper/2102.04699/) (2021)
- [SDA-GAN: Unsupervised Image Translation Using Spectral Domain Attention-Guided Generative Adversarial Network](https://mlsanity.com/paper/2110.02873/) (2021)
- [Global and Local Alignment Networks for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2111.10346/) (2021)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) have recently achieved significant improvement on paired/unpaired image-to-image translation, such as photorightarrow sketch and artist painting style transfer. However, existing models can only be capable of transferring the low-level information (e.g. color or texture changes), but fail to edit high-level semantic meanings (e.g., geometric structure or content) of objects. On the other hand, while some researches can synthesize compelling real-world images given a class label or caption, they cannot condition on arbitrary shapes or structures, which largely limits their application scenarios and interpretive capability of model results. In this work, we focus on a more challenging semantic manipulation task, which aims to modify the semantic meaning of an object while preserving its own characteristics (e.g. viewpoints and shapes), such as cowrightarrowsheep, motorrightarrow bicycle, catrightarrowdog. To tackle such large semantic changes, we introduce a contrasting GAN (contrast-GAN) with a novel adversarial contrasting objective. Instead of directly making the synthesized samples close to target data as previous GANs did, our adversarial contrasting objective optimizes over the distance comparisons between samples, that is, enforcing the manipulated data be semantically closer to the real data with target category than the input data. Equipped with the new contrasting objective, a novel mask-conditional contrast-GAN architecture is proposed to enable disentangle image background with object semantic changes. Experiments on several semantic manipulation tasks on ImageNet and MSCOCO dataset show considerable performance gain by our contrast-GAN over other conditional GANs. Quantitative results further demonstrate the superiority of our model on generating manipulated results with high visual fidelity and reasonable object semantics.