Recent advances in deep learning have made the use of large, deep neural networks with tens of millions of parameters. The sheer size of these networks imposes a challenging computational burden during inference. Existing work focuses primarily on accelerating each forward pass of a neural network. Inspired by the group testing strategy for efficient disease testing, we propose neural group testing, which accelerates by testing a group of samples in one forward pass. Groups of samples that test negative are ruled out. If a group tests positive, samples in that group are then retested adaptively. A key challenge of neural group testing is to modify a deep neural network so that it could test multiple samples in one forward pass. We propose three designs to achieve this without introducing any new parameters and evaluate their performances. We applied neural group testing in an image moderation task to detect rare but inappropriate images. We found that neural group testing can group up to 16 images in one forward pass and reduce the overall computation cost by over 73% while improving detection performance.

中文翻译：

---

神经组测试以加速深度学习

深度学习的最新进展已使用具有数千万个参数的大型深度神经网络。这些网络的庞大规模在推理过程中带来了挑战性的计算负担。现有工作主要集中在加速神经网络的每次前进。受有效疾病测试的分组测试策略的启发，我们提出了神经分组测试，它通过一次向前测试一组样本来加速。排除测试阴性的样本组。如果一组的测试结果为阳性，则该组中的样本将被自适应地重新测试。神经组测试的一个关键挑战是修改一个深度神经网络，以便它可以一次通过测试多个样本。我们提出了三种设计来实现这一目标，而不引入任何新参数并评估其性能。我们在图像审核任务中应用了神经群体测试，以检测稀有但不合适的图像。我们发现神经组测试可以在一次向前通过中对多达16张图像进行分组，并且在提高检测性能的同时将总体计算成本降低了73％以上。