论文标题
用更少的数据保持性能
Maintaining Performance with Less Data
论文作者
论文摘要
我们提出了一种新的方法，用于训练神经网络进行图像分类以动态减少输入数据，以降低训练神经网络模型的成本。随着深度学习任务变得越来越流行，它们的计算复杂性会增加，从而导致更复杂的算法和模型，这些算法和模型具有更长的时间，并且需要更多的输入数据。结果是按时，硬件和环境资源的成本更高。通过使用减少数据技术，我们减少了执行的工作量以及AI技术的环境影响，并且通过动态数据降低，我们表明，可以在将运行时保持高达50％的同时保持准确性，并按比例减少碳排放。
We propose a novel method for training a neural network for image classification to reduce input data dynamically, in order to reduce the costs of training a neural network model. As Deep Learning tasks become more popular, their computational complexity increases, leading to more intricate algorithms and models which have longer runtimes and require more input data. The result is a greater cost on time, hardware, and environmental resources. By using data reduction techniques, we reduce the amount of work performed, and therefore the environmental impact of AI techniques, and with dynamic data reduction we show that accuracy may be maintained while reducing runtime by up to 50%, and reducing carbon emission proportionally.
