TY  - GEN
SN  - 1063-6919
UR  - https://doi.org/10.1109/CVPR.2018.00587
A1  - Firman, M
A1  - Campbell, NDF
A1  - Agapito, L
A1  - Brostow, GJ
SP  - 5598
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
PB  - IEEE
ID  - discovery10074121
N2  - 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.
KW  - Training
KW  -  Task analysis
KW  -  Aerospace electronics
KW  -  Three-dimensional displays
KW  -  Supervised learning
KW  -  Two dimensional displays
KW  -  Training data
T3  - IEEE/CVF Conference on Computer Vision and Pattern Recognition
CY  - Salt Late City, UT, USA
EP  - 5607
AV  - public
Y1  - 2018/12/17/
TI  - DiverseNet: When One Right Answer is not Enough
ER  -