Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. The relevance of each neuron is identified with respect to its degree of contribution, separated into positive and negative, while preserving the conservation rule. Considering the relevance assigned to neurons in terms of relative priority, RAP allows each neuron to be assigned with a bi-polar importance score concerning the output: from highly relevant to highly irrelevant. Therefore, our method makes it possible to interpret DNNs with much clearer and attentive visualizations of the separated attributions than the conventional explaining methods. To verify that the attributions propagated by RAP correctly account for each meaning, we utilize the evaluation metrics: (i) Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region perturbation. In all experiments and metrics, we present a sizable gap in comparison to the existing literature. Our source code is available in \url{https://github.com/wjNam/Relative_Attributing_Propagation}.Comment: 8 pages, 7 figures, Accepted paper in AAAI Conference on Artificial Intelligence (AAAI), 202

Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations

The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and nonlinear transformations along deep hierarchies. In this paper, we propose a new attribution method, Relative Sectional Propagation (RSP), for fully decomposing the output predictions with the characteristics of class-discriminative attributions and clear objectness. We carefully revisit some shortcomings of backpropagation-based attribution methods, which are trade-off relations in decomposing DNNs. We define hostile factor as an element that interferes with finding the attributions of the target and propagate it in a distinguishable way to overcome the non-suppressed nature of activated neurons. As a result, it is possible to assign the bi-polar relevance scores of the target (positive) and hostile (negative) attributions while maintaining each attribution aligned with the importance. We also present the purging techniques to prevent the decrement of the gap between the relevance scores of the target and hostile attributions during backward propagation by eliminating the conflicting units to channel attribution map. Therefore, our method makes it possible to decompose the predictions of DNNs with clearer class-discriminativeness and detailed elucidations of activation neurons compared to the conventional attribution methods. In a verified experimental environment, we report the results of the assessments: (i) Pointing Game, (ii) mIoU, and (iii) Model Sensitivity with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing backward decomposition methods, including distinctive and intuitive visualizations.Comment: 9 pages, 8 figures, Accepted paper in AAAI Conference on Artificial Intelligence (AAAI), 202

Development and Clinical Evaluation of a Rapid Serodiagnostic Test for Toxoplasmosis of Cats Using Recombinant SAG1 Antigen

Rapid serodiagnostic methods for Toxoplasma gondii infection in cats are urgently needed for effective control of transmission routes toward human infections. In this work, 4 recombinant T. gondii antigens (SAG1, SAG2, GRA3, and GRA6) were produced and tested for the development of rapid diagnostic test (RDT). The proteins were expressed in Escherichia coli, affinity-purified, and applied onto the nitrocellulose membrane of the test strip. The recombinant SAG1 (rSAG1) showed the strongest antigenic activity and highest specificity among them. We also performed clinical evaluation of the rSAG1-loaded RDT in 182 cat sera (55 household and 127 stray cats). The kit showed 0.88 of kappa value comparing with a commercialized ELISA kit, which indicated a significant correlation between rSAG1-loaded RDT and the ELISA kit. The overall sensitivity and specificity of the RDT were 100% (23/23) and 99.4% (158/159), respectively. The rSAG1-loaded RDT is rapid, easy to use, and highly accurate. Thus, it would be a suitable diagnostic tool for rapid detection of antibodies in T. gondii-infected cats under field conditions

Gradient Hedging for Intensively Exploring Salient Interpretation beyond Neuron Activation

Hedging is a strategy for reducing the potential risks in various types of investments by adopting an opposite position in a related asset. Motivated by the equity technique, we introduce a method for decomposing output predictions into intensive salient attributions by hedging the evidence for a decision. We analyze the conventional approach applied to the evidence for a decision and discuss the paradox of the conservation rule. Subsequently, we define the viewpoint of evidence as a gap of positive and negative influence among the gradient-derived initial contribution maps and propagate the antagonistic elements to the evidence as suppressors, following the criterion of the degree of positive attribution defined by user preference. In addition, we reflect the severance or sparseness contribution of inactivated neurons, which are mostly irrelevant to a decision, resulting in increased robustness to interpretability. We conduct the following assessments in a verified experimental environment: pointing game, most relevant first region insertion, outside-inside relevance ratio, and mean average precision on the PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing attribution methods in distinctive, intensive, and intuitive visualization with robustness and applicability in general models

Coarse-to-Fine Deep Metric Learning for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) is the process of searching for identical areas by investigating the similarities between a query image and the database images. RSIR is a challenging task owing to the time difference, viewpoint, and coverage area depending on the shooting circumstance, resulting in variations in the image contents. In this paper, we propose a novel method based on a coarse-to-fine strategy, which makes a deep network more robust to the variations in remote sensing images. Moreover, we propose a new triangular loss function to consider the whole relation within the tuple. This loss function improves the retrieval performance and demonstrates better performance in terms of learning the detailed information in complex remote sensing images. To verify our methods, we experimented with the Google Earth South Korea dataset, which contains 40,000 images, using the evaluation metric Recall@n. In all experiments, we obtained better performance results than those of the existing retrieval training methods. Our source code and Google Earth South Korea dataset are available online

Comparison of the Visual Outcomes of Enhanced and Standard Monofocal Intraocular Lens Implantations in Eyes with Early Glaucoma

This study aimed to compare the efficacies and safety of enhanced and standard monofocal intraocular lenses (IOLs) in eyes with early glaucoma. Patients with concurrent cataracts and open-angle glaucoma (OAG) were enrolled. They underwent cataract surgery with IOL implantation. The comprehensive preoperative ophthalmic examination included the manifest refraction; monocular uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), uncorrected intermediate visual acuity (UIVA), and uncorrected near visual acuity (UNVA); visual field (VF); and contrast sensitivity (CS); defocus curves and questionnaires were assessed three months postoperatively. Totals of 34 and 38 patients had enhanced and standard monofocal IOLs, respectively. The enhanced monofocal IOL provided better UIVA than the standard monofocal IOL (p = 0.003) but similar UDVA, CDVA, and UNVA. The enhanced monofocal IOL had more consistent defocus curves than the standard monofocal IOL, especially at −1 (p = 0.042) and −1.5 (p = 0.026) diopters. The enhanced monofocal IOL provided better satisfaction (p = 0.019) and lower spectacle dependence (p = 0.004) than the standard monofocal IOL for intermediate vision, with similar VF and CS outcomes. In conclusion, enhanced monofocal IOLs are recommended for patients with OAG because they provide better intermediate vision, higher satisfaction, and lower dependence on spectacles than standard monofocal IOLs, without worsening other visual outcomes

Towards Better Visualizing the Decision Basis of Networks via Unfold and Conquer Attribution Guidance

Revealing the transparency of Deep Neural Networks (DNNs) has been widely studied to describe the decision mechanisms of network inner structures. In this paper, we propose a novel post-hoc framework, Unfold and Conquer Attribution Guidance (UCAG), which enhances the explainability of the network decision by spatially scrutinizing the input features with respect to the model confidence. Addressing the phenomenon of missing detailed descriptions, UCAG sequentially complies with the confidence of slices of the image, leading to providing an abundant and clear interpretation. Therefore, it is possible to enhance the representation ability of explanation by preserving the detailed descriptions of assistant input features, which are commonly overwhelmed by the main meaningful regions. We conduct numerous evaluations to validate the performance in several metrics: i) deletion and insertion, ii) (energy-based) pointing games, and iii) positive and negative density maps. Experimental results, including qualitative comparisons, demonstrate that our method outperforms the existing methods with the nature of clear and detailed explanations and applicability

Effect of open-field experimental warming on the leaf phenology of oriental oak (Quercus variabilis) seedlings

Aims: An open-field warming experiment enables us to test the effects of projected temperature increase on change in plant phenology with fewer confounding factors and to study phenological response to temperature ranges beyond natural variability. This study aims to (i) examine the effect of temperature increase on leaf unfolding and senescence of oriental oak (Quercus variabilis Blume) under experimental warming and (ii) measure temperature-related parameters used in estimating phenological response to temperature elevation. Methods: Using an open-field warming system with infrared heaters, we increased the air temperature by ∼3°C in the warmed plots compared with that of the control plots consistently for 2 years. Leaf unfolding and senescence dates of Q. variabilis seedlings were recorded and temperature-related phenological parameters were analysed. Important Findings: The timing of leaf unfolding was advanced by 3-8 days (1.1-3.0 days/°C) and the date of leaf senescence was delayed by 14-19 days (5.0-7.3 days/°C) under elevated air temperatures. However, the cumulative degree days (CDD) of leaf unfolding were not significantly changed by experimental warming, which suggest the applicability of a constant CDD value to estimate the change in spring leaf phenology under 3°C warming. Consistent ranges of advancement and temperature sensitivity in spring phenology and delayed autumn phenology and proposed temperature parameters from this study might be applied to predict future phenological change