Multitask learning is a popular approach to training high-performing neural
networks with improved generalization. In this paper, we propose a background
class to achieve improved generalization at a lower computation compared to
multitask learning to help researchers and organizations with limited
computation power. We also present a methodology for selecting background
images and discuss potential future improvements. We apply our approach to
several datasets and achieved improved generalization with much lower
computation. We also investigate class activation mappings (CAMs) of the
trained model and observed the tendency towards looking at a bigger picture in
a few class classification problems with the proposed model training
methodology. Example scripts are available in the `CAM' folder of the following
GitHub Repository: github.com/dipuk0506/UQ

本文提出了一种背景类方法，以提高神经网络的泛化性能。通过采用所提出方法，可以在较低的计算成本下实现更好的泛化能力，并且可以用于多个数据集的分类问题。通过训练模型，观察类激活映射的倾向，发现了在该方法的几个类别分类问题中，神经网络趋向于关注更大的视角。

利用背景信息降低分类激活的不确定性

Reduction of Class Activation Uncertainty with Background Information

In this paper, we aim to obtain improved attention for a visual question
answering (VQA) task. It is challenging to provide supervision for attention.
An observation we make is that visual explanations as obtained through class
activation mappings (specifically Grad-CAM) that are meant to explain the
performance of various networks could form a means of supervision. However, as
the distributions of attention maps and that of Grad-CAMs differ, it would not
be suitable to directly use these as a form of supervision. Rather, we propose
the use of a discriminator that aims to distinguish samples of visual
explanation and attention maps. The use of adversarial training of the
attention regions as a two-player game between attention and explanation serves
to bring the distributions of attention maps and visual explanations closer.
Significantly, we observe that providing such a means of supervision also
results in attention maps that are more closely related to human attention
resulting in a substantial improvement over baseline stacked attention network
(SAN) models. It also results in a good improvement in rank correlation metric
on the VQA task. This method can also be combined with recent MCB based methods
and results in consistent improvement. We also provide comparisons with other
means for learning distributions such as based on Correlation Alignment
(Coral), Maximum Mean Discrepancy (MMD) and Mean Square Error (MSE) losses and
observe that the adversarial loss outperforms the other forms of learning the
attention maps. Visualization of the results also confirms our hypothesis that
attention maps improve using this form of supervision.

使用对抗训练作为监督学习来提高注意力映射，改善视觉问答任务的表现，同时与其他学习分布的方法相比，发现对抗损失函数表现更好。