Saliency maps have proven to be a highly efficacious approach for explicating the decisions of convolutional neural networks (CNNs). However, extant methodologies predominantly rely on gradients, which constrain their ability to explicate complex models. Furthermore, such approaches are not fully adept at leveraging negative gradient information to improve interpretive veracity. In this study, we present a novel concept, termed positive and negative excitation (PANE), which enables the direct extraction of PANE for each layer, thus enabling complete layer-by-layer information utilization sans gradients. To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure. A comprehensive experimental evaluation, encompassing both binary classification and multiclassification tasks, was conducted to gauge the effectiveness of our proposed method. Encouragingly, the results evince that our approach offers a significant improvement over the state-of-the-art methods in terms of salient pixel removal, minor pixel removal, and inconspicuous adversarial perturbation generation guidance. In addition, we verify the correlation between PANEs.

Various correlations hidden in crowdsourcing annotation tasks bring opportunities to further improve the accuracy of label aggregation. However, these relationships are usually extremely difficult to be modeled. Most existing methods can merely make use of one or two correlations. In this paper, we propose a novel graph neural network model, namely LAGNN, which models five different correlations in crowdsourced annotation tasks by utilizing deep graph neural networks with convolution operations and derives a high label aggregation performance. Utilizing the group of high quality workers through labeling similarity, LAGNN can efficiently revise the preference among workers. Moreover, by injecting a little ground truth in its training stage, the label aggregation performance of LAGNN can be further significantly improved. We evaluate LAGNN on a large number of simulated datasets generated through varying six degrees of freedom and on eight real-world crowdsourcing datasets in both supervised and unsupervised (agnostic) modes. Experiments on data leakage is also contained. Experimental results consistently show that the proposed LAGNN significantly outperforms six state-of-the-art models in terms of label aggregation accuracy.