论文标题
通过解释学习强大的卷积神经网络，并通过相关功能聚焦
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations
论文作者
论文摘要
基于卷积神经网络（CNN）的现有图像识别技术基本上假设训练和测试数据集是从I.I.D分布中采样的。但是，由于在输入图像中的对象和背景之间的共发生关系发生变化时，这种假设在现实世界中很容易被破坏。在这种类型的分配变化下，CNN学会着专注于与任务相关的功能，例如培训数据的背景，并在测试数据上降低其准确性。为了解决这个问题，我们提出了相关功能聚焦（REFF）。 REFF检测与任务相关的功能，并通过说明输出（例如Grad-CAM）正规化CNN。由于REFF由事后解释模块组成，因此可以轻松地应用于现成的CNN。此外，REFF在测试时间不需要额外的推理成本，因为它仅在训练时用于正则化。我们证明，经过REFF训练的CNN专注于与目标任务相关的功能，并提高了测试时间的准确性。
Existing image recognition techniques based on convolutional neural networks (CNNs) basically assume that the training and test datasets are sampled from i.i.d distributions. However, this assumption is easily broken in the real world because of the distribution shift that occurs when the co-occurrence relations between objects and backgrounds in input images change. Under this type of distribution shift, CNNs learn to focus on features that are not task-relevant, such as backgrounds from the training data, and degrade their accuracy on the test data. To tackle this problem, we propose relevant feature focusing (ReFF). ReFF detects task-relevant features and regularizes CNNs via explanation outputs (e.g., Grad-CAM). Since ReFF is composed of post-hoc explanation modules, it can be easily applied to off-the-shelf CNNs. Furthermore, ReFF requires no additional inference cost at test time because it is only used for regularization while training. We demonstrate that CNNs trained with ReFF focus on features relevant to the target task and that ReFF improves the test-time accuracy.
