解决图像分类中标签数据稀缺问题，实现跨域模型迁移。
A Review on Domain Adaption and Generative Adversarial Networks(GANs)
- 利用已标注数据训练模型，迁移到未标注目标域
- 实现在不同领域间（如画作到真实图像）的准确预测
- 适合数据标注困难场景的研究者参考
当前计算机视觉面临的重大挑战是高质量标注数据的获取。在图像分类等依赖数据的任务中，需寻找更可靠的方法以克服数据稀缺问题，从而达到与以往基准相当的性能。由于人工标注成本高昂甚至不可行，获取标签数据极为困难。本文旨在探讨领域自适应（Domain Adaptation）及其多种实现方法。核心思想是使用在特定数据集上训练的模型，对同类型但不同领域的数据进行预测，例如：在飞机绘画数据上训练的模型，用于预测真实飞机图像。通过该方法，可有效缓解标注数据不足带来的性能下降。
原文摘要 · Abstract (English)
The major challenge in today's computer vision scenario is the availability of good quality labeled data. In a field of study like image classification, where data is of utmost importance, we need to find more reliable methods which can overcome the scarcity of data to produce results comparable to previous benchmark results. In most cases, obtaining labeled data is very difficult because of the high cost of human labor and in some cases impossible. The purpose of this paper is to discuss Domain Adaptation and various methods to implement it. The main idea is to use a model trained on a particular dataset to predict on data from a different domain of the same kind, for example - a model trained on paintings of airplanes predicting on real images of airplanes
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