Many structured prediction tasks in machine vision have a collection of
acceptable answers, instead of one definitive ground truth answer. Segmentation
of images, for example, is subject to human labeling bias. Similarly, there are
multiple possible pixel values that could plausibly complete occluded image
regions. State-of-the art supervised learning methods are typically optimized
to make a single test-time prediction for each query, failing to find other
modes in the output space. Existing methods that allow for sampling often
sacrifice speed or accuracy.
We introduce a simple method for training a neural network, which enables
diverse structured predictions to be made for each test-time query. For a
single input, we learn to predict a range of possible answers. We compare
favorably to methods that seek diversity through an ensemble of networks. Such
stochastic multiple choice learning faces mode collapse, where one or more
ensemble members fail to receive any training signal. Our best performing
solution can be deployed for various tasks, and just involves small
modifications to the existing single-mode architecture, loss function, and
training regime. We demonstrate that our method results in quantitative
improvements across three challenging tasks: 2D image completion, 3D volume
estimation, and flow prediction.

本文介绍了一种简单的方法来训练神经网络，使得每个测试查询时间内能够进行多元化的结构性预测，同时比现有方法具有更好的多样性和速度。在 2D 图像填充，3D 体积估计和流场预测等三个具有挑战性的任务中，我们的方法得到了量化的改进。