论文标题
课堂激活映射的概念器学习
Conceptor Learning for Class Activation Mapping
论文作者
论文摘要
类激活映射（CAM）已被广泛采用以生成显着图，从而为深神经网络（DNN）提供了视觉解释。显着图是通过使用加权平均方案融合目标特征图的通道来生成的。它是通道间关系的一个弱模型，因为它仅以对比的方式对通道之间的关系进行建模（即，在预测中扮演关键角色的通道具有更高的权重以使其在融合中脱颖而出）。使渠道共同提供交叉参考的协作关系已被忽略。此外，该模型彻底忽略了通道内的关系。在本文中，我们通过将概念器学习引入CAM生成来解决这个问题。最初提出了Conceptor倾斜，以模拟复发性神经网络（RNN）中状态变化的模式。通过放松概念器学习对RNN的依赖，我们不仅可以使Conceptor-CAM不仅可以推广到更多的DNN体系结构，还可以学习渠道内和渠道内关系，以获得更好的显着性图。此外，我们已经实现了布尔操作来结合正面和伪阴性的证据，这使CAM推断更加强大和全面。概念-CAM的有效性已通过在文献规模最大的数据集上进行正式验证和实验验证。 The experimental results show that Conceptor-CAM is compatible with and can bring significant improvement to all well recognized CAM-based methods, and has outperformed the state-of-the-art methods by 43.14%~72.79% (88.39%~168.15%) on ILSVRC2012 in Average Increase (Drop), 15.42%~42.55% (47.09%~372.09%) on VOC,和可可的17.43％〜31.32％（47.54％〜206.45％）。
Class Activation Mapping (CAM) has been widely adopted to generate saliency maps which provides visual explanations for deep neural networks (DNNs). The saliency maps are conventionally generated by fusing the channels of the target feature map using a weighted average scheme. It is a weak model for the inter-channel relation, in the sense that it only models the relation among channels in a contrastive way (i.e., channels that play key roles in the prediction are given higher weights for them to stand out in the fusion). The collaborative relation, which makes the channels work together to provide cross reference, has been ignored. Furthermore, the model has neglected the intra-channel relation thoroughly.In this paper, we address this problem by introducing Conceptor learning into CAM generation. Conceptor leaning has been originally proposed to model the patterns of state changes in recurrent neural networks (RNNs). By relaxing the dependency of Conceptor learning to RNNs, we make Conceptor-CAM not only generalizable to more DNN architectures but also able to learn both the inter- and intra-channel relations for better saliency map generation. Moreover, we have enabled the use of Boolean operations to combine the positive and pseudo-negative evidences, which has made the CAM inference more robust and comprehensive. The effectiveness of Conceptor-CAM has been validated with both formal verifications and experiments on the dataset of the largest scale in literature. The experimental results show that Conceptor-CAM is compatible with and can bring significant improvement to all well recognized CAM-based methods, and has outperformed the state-of-the-art methods by 43.14%~72.79% (88.39%~168.15%) on ILSVRC2012 in Average Increase (Drop), 15.42%~42.55% (47.09%~372.09%) on VOC, and 17.43%~31.32% (47.54%~206.45%) on COCO, respectively.
