Dandelion: Boosting DNN Usability Under Dataset Scarcity

The development of deep neural network (DNN) has provided transformative impacts on many fields, including computer vision and video recognition. However, the impact is limited by the need for large, labeled datasets to enable effective training. To address this fundamental problem, we propose a novel inter-network system (Dandelion), providing architecture support (Dandelion-architecture) for data augmentation that trains DNNs with rare images generated by the generative adversarial network (GAN) with orthogonal attributes modified (Dandelion-function. The approach can account for the latency requirement and resource limitation of target applications by exploiting data and computation reuses between the two networks; this amortizes the impact of bottleneck brought by GAN and facilitates design of inter-network accelerator. Moreover, we show how to implement two-network design on 3D architecture to further enhance the accelerator. Our results show that with the generated images, DNN yields 13.6% - 37.5% improvement on accuracy, depending on the data scarcity level. Our architecture achieves at least 30% speedup compared with the baseline while 40% of the overhead brought by the incorporation of GAN is reduced in our design compared with ScaleDeep, and 26.3% of performance improvement over TETRIS.