We present a task-aware approach to synthetic data generation. Our framework
employs a trainable synthesizer network that is optimized to produce meaningful
training samples by assessing the strengths and weaknesses of a `target'
network. The synthesizer and target networks are trained in an adversarial
manner wherein each network is updated with a goal to outdo the other.
Additionally, we ensure the synthesizer generates realistic data by pairing it
with a discriminator trained on real-world images. Further, to make the target
classifier invariant to blending artefacts, we introduce these artefacts to
background regions of the training images so the target does not over-fit to
them.
We demonstrate the efficacy of our approach by applying it to different
target networks including a classification network on AffNIST, and two object
detection networks (SSD, Faster-RCNN) on different datasets. On the AffNIST
benchmark, our approach is able to surpass the baseline results with just half
the training examples. On the VOC person detection benchmark, we show
improvements of up to 2.7% as a result of our data augmentation. Similarly on
the GMU detection benchmark, we report a performance boost of 3.5% in mAP over
the baseline method, outperforming the previous state of the art approaches by
up to 7.5% on specific categories.

本文提出了一种针对任务的合成数据生成方法，通过训练可训练的合成器网络产生有意义的样本，并与现实世界的图像相关的判别器配对来生成逼真的数据，再通过加入混合术来提高训练目标分类器的不变性，实验结果表明该方法在不同的目标网络上的应用可以显著提高检测性能。