ZstGAN | An Adversarial Approach forUnsupervised Zero-Shot Image-to-Image Translation

Posted by JoselynZhao on October 24, 2019

Abstract

In this work In this workwe, we propose a framework calledZstGAN: By introducing an adversarial training scheme,ZstGAN learns to model each domain with domain-specificfeature distribution that is semantically consistent on visionand attribute modalities.

Our code is publicly available at https://github.com/linjx-ustc1106/ZstGAN-PyTorch.

Introduction

Existing image-to-image translation usually workson the following setting:

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One limitation of existing models is, the fij ’s can onlyachieve mappings among these given domains, without thegeneralization abilities to other unseen domains.

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Therefore we aim to generalizef to unseen domains as shown in the bottom half part ofFigure 1.

In order to generalize to unseen classes, a com-mon assumption in zero-shot learning assuming is that someside-information about the classes is available, such as classattributes or textual descriptions, which provides semanticinformation about the classes.

wepropose a new problem, unsupervised zero-shot image-to-image translation (briefly, UZSIT).

Compared to the stan-dard ZSL, UZSIT is more challenging: (1) The targetof image translation is more complex than classification,which not only requires us to generate representative fea-tures across seen and unseen domains but also generate rea-sonable translation images.

(2) Unlike ZSL methods trainedin a supervised way on seen domains, we do not have anypaired data between any two domains.

There aretwo key steps in ZstGAN.

1、 We model each seen/unseen domain using a domain-specific feature distribution constrained by semantic con-sistency.

2、We disentangle domain-invariant features from thedomain-specific features and combine them to generatetranslation results, which is achieved by one adversariallearning loss and two reconstruction losses.

We work on two datasets commonly used in ZSL,Caltech-UCSD-Birds 200-2011 (CUB) [31] and OxfordFlowers (FLO) [25],

Methods

Problem Formulation

We provide a mathematical formulation of UZSIT in thissubsection. 在这里插入图片描述

The objective of UZSIT is to train an image-to-imagetranslation model f on S without touching U .

An assumption that S and U shares a common semanticspace is required. Specifically, while S and U have differ-ent category sets (Lsand Lu), they are required to share thesame image and attribute spaces (X and A) where semanticinformation is extracted from.

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在现有的图像-图像翻译模型中,通常只提取不同领域的特定特征,而不将其描述在一个共同的语义空间中。 领域特定的特征不仅应该区分不同的领域,而且应该具有代表性,以便在一个共同的语义空间中对齐不同的领域。

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Architecture

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在这里插入图片描述 The objective function is designed according to the fol-lowing criteria:

(1) Domain-specific features with semantic consistency

In detail, the adversarialtraining objective for Ev and Ea isIn detail, the adversarialtraining objective for Ev and Ea is 在这里插入图片描述

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(2) Domain-invariant features disentanglement

在这里插入图片描述 在这里插入图片描述

(3)The overall training objective

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