论文标题
朝设备有效的有条件图像生成
Towards Device Efficient Conditional Image Generation
论文作者
论文摘要
我们提出了一种新型算法，以减少有条件的图像生成自动编码器所需的张量计算，而无需牺牲光真实图像产生的质量。我们的方法是设备不可知的，可以在仅在普通工作站上训练自动编码器的正常时间为给定的仅CPU的GPU计算设备优化自动编码器。我们通过一种两阶段的小说策略来实现这一目标，首先，我们将通道权重凝结，以便使用尽可能少的通道。然后，我们修剪几乎零的重量激活，然后微调自动编码器。为了保持图像质量，通过学生教师培训进行微调，我们将凝结的自动编码器重新使用为老师。我们显示了各种条件图像生成任务的性能增长：分割掩码面对图像，面对卡通化的图像，最后是基于自行车的模型在多个计算设备上。我们进行各种消融研究，以证明主张和设计选择合理，并在维持图像质量的同时，在CPU仅设备上实现各种自动编码器的实时版本，从而实现了此类自动编码器的尺度部署。
We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder without sacrificing quality of photo-realistic image generation. Our method is device agnostic, and can optimize an autoencoder for a given CPU-only, GPU compute device(s) in about normal time it takes to train an autoencoder on a generic workstation. We achieve this via a two-stage novel strategy where, first, we condense the channel weights, such that, as few as possible channels are used. Then, we prune the nearly zeroed out weight activations, and fine-tune the autoencoder. To maintain image quality, fine-tuning is done via student-teacher training, where we reuse the condensed autoencoder as the teacher. We show performance gains for various conditional image generation tasks: segmentation mask to face images, face images to cartoonization, and finally CycleGAN-based model over multiple compute devices. We perform various ablation studies to justify the claims and design choices, and achieve real-time versions of various autoencoders on CPU-only devices while maintaining image quality, thus enabling at-scale deployment of such autoencoders.
