基于类别依赖的MixUp特征绑定的语义分割和对抗鲁棒性
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness
摘要：
在本文中，我们提出了一种训练卷积神经网络的策略，以有效地解决整个网络中与类别间信息相关的竞争假设所产生的干扰。前提是基于特征绑定的概念，特征绑定被定义为激活在网络中的空间和层中的传播被成功集成以达到正确的推理决策的过程。在我们的工作中，这是通过基于它们的类标签混合图像来完成密集图像标记的任务，然后训练一个特征绑定网络，该网络同时分割和分离混合图像。随后的特征去噪以抑制噪声激活，揭示了额外的理想特性和高度成功的预测。通过这个过程，我们揭示了一种与任何先前方法不同的通用机制，用于提高基础分割网络的性能，同时提高对对抗攻击的鲁棒性。
英文摘要：
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. The premise is based on the notion of feature binding, which is defined as the process by which activation's spread across space and layers in the network are successfully integrated to arrive at a correct inference decision. In our work, this is accomplished for the task of dense image labelling by blending images based on their class labels, and then training a feature binding network, which simultaneously segments and separates the blended images. Subsequent feature denoising to suppress noisy activations reveals additional desirable properties and high degrees of successful predictions. Through this process, we reveal a general mechanism, distinct from any prior methods, for boosting the performance of the base segmentation network while simultaneously increasing robustness to adversarial attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
