In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. Generative adversarial network (GAN) loss is widely used in adversarial adaptation learning methods to reduce an across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, we put forward a novel adaptation framework called generative adversarial distribution matching (GADM). In GADM, we improve the objective function by taking cross-domain discrepancy distance into consideration and further minimize the difference through the competition between a generator and discriminator, thereby greatly decreasing cross-domain distribution difference. Experimental results and comparison with several state-of-the-art methods verify GADM’s superiority in image classification across domains.

中文翻译：

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通过生成对抗分布匹配进行有效的视觉域适应

在计算机视觉领域，没有足够的标记图像，训练准确的模型具有挑战性。然而，通过从源域到目标域的视觉适应，相关的标记数据集可以帮助解决此类问题。许多方法应用对抗性学习来减少跨域分布差异。它们能够大大提高目标分类任务的性能。生成对抗网络 (GAN) 损失广泛用于对抗性适应学习方法，以减少跨域分布差异。然而，如果 GAN 中的生成器或鉴别器无法按预期工作并降低其性能，则很难减少这种分布差异。为了解决这样的跨域分类问题，我们提出了一种新的适应框架，称为生成对抗分布匹配（GADM）。在GADM中，我们通过考虑跨域差异距离来改进目标函数，并通过生成器和鉴别器之间的竞争进一步最小化差异，从而大大降低跨域分布差异。实验结果和与几种最先进方法的比较验证了 GADM 在跨域图像分类方面的优越性。