0%

## 总结

• 在GAN的基础上加上了条件输入（对于D和G同时加）
• 目标函数改为$\underset {G} {min} \underset {D} {max}V(D,G) = E_{x \sim p_{data}(x)}[logD(x|y)] + E_{z \sim p_z(z)}[1 - log(D(G(z|y)))]$
• 如果没有对于条件的需求在实验中有CGAN的效果并不比GAN的效果好
• CGAN可以按照条件输入输出特定的随机值
• 给定一个数字输出特定数字的手写图片
• 给定一个图片输出该图片的tag 概率分布，取较大者作为图片的tag，实现自动化tagging

## Abstract

this net can be constructed by adding condition information to both generator and discriminator

## Introduction

by conditioning the model on additional information it is possible to direct the data gener- ation process

### multi-modal learning for image labelling

present works problems:

• hard to predict output categories
• almost one-to-many mapping

solution:

• first issue: leverage additional information from other modalities
• second issue: use a conditional probabilistic generative model

a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. Both G and D could be a non-linear mapping function, such as a multi-layer perceptron

Generative adversarial nets can be extended to a conditional model if both the generator and discrim- inator are conditioned on some extra information y

objective function:

## Experimental Results

### Unimodal

trained a conditional adversarial net on MNIST images conditioned on their class labels, encoded as one-hot vectors

outperformed by several other approaches