This work was performed under the following financial assistance award: 60NANB18D279 from U.S. Department of Commerce, National Institute of Standards and Technology, and also funding from SAP SE
[width=.17 ] img_orig_000038.jpg & [width=.17 ] img_reg_patch_000038_orig_014_target_017_pred_017_prob_100.png & [width=.17 ] mask_reg_patch_000038_orig_014_target_017_pred_017.JPEG & [width=.17 ] img_our_patch_000038_orig_014_target_017__pred_017_prob_86.png & [width=.17 ] mask_our_patch_000038_orig_014_target_017_pred_017.png Person & Sofa & Sofa & Sofa & Sofa [width=.17 ] img_orig_000145.jpg & [width=.17 ] img_reg_patch_000145_orig_018_target_008_pred_008_prob_100.png & [width=.17 ] mask_reg_patch_000145_orig_018_target_008_pred_008.JPEG & [width=.17 ] img_our_patch_000145_orig_018_target_008__pred_008_prob_97.png & [width=.17 ] mask_our_patch_000145_orig_018_target_008_pred_008.png Train & Chair & Chair & Chair & Chair [width=.17 ] img_orig_000168.jpg & [width=.17 ] img_reg_patch_000168_orig_012_target_003_pred_003_prob_100.png & [width=.17 ] mask_reg_patch_000168_orig_012_target_003_pred_003.JPEG & [width=.17 ] img_our_patch_000168_orig_012_target_003__pred_003_prob_91.png & [width=.17 ] mask_our_patch_000168_orig_012_target_003_pred_003.png Horse & Boat & Boat & Boat & Boat [width=.17 ] img_orig_002007.jpg & [width=.17 ] img_reg_patch_002007_orig_000_target_017_pred_017_prob_100.png & [width=.17 ] mask_reg_patch_002007_orig_000_target_017_pred_017.JPEG & [width=.17 ] img_our_patch_002007_orig_000_target_017__pred_017_prob_99.png & [width=.17 ] mask_our_patch_002007_orig_000_target_017_pred_017.png Aeroplane & Sofa & Sofa & Sofa & Sofa @cite_6 network for VOC dataset. The predicted label is written under each image, the attack was successful for all images, and Grad-CAM is always computed for the target category. GAIN @math is particularly designed to produce better Grad-CAM visualizations using direct supervision of the Grad-CAM output.
@cite_6
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Spanners, emulators, and approximate distance oracles can be viewed as lossy compression schemes that represent an unweighted graph metric in small space, say O(n1+Δ) bits. There is an inherent tradeoff between the sparsity parameter Δ and the stretch function f of the compression scheme, but the qualitative nature of this tradeoff has remained a persistent open problem. It has been known for some time that when Δ ≥ 1 3 there are schemes with constant additive stretch (distance d is stretched to at most f(d) = d + O(1)), and recent results of Abboud and Bodwin show that when Δ In this paper we show that the lower bound of Abboud and Bodwin is just the first step in a hierarchy of lower bounds that characterize the asymptotic behavior of the optimal stretch function f for sparsity parameter Δ ∈ (0,1 3). Specifically, for any integer k ≥ 2, any compression scheme with size [EQUATION] has a sublinear additive stretch function f: f(d) = d + Ω(d1−1 k). This lower bound matches Thorup and Zwick's (2006) construction of sublinear additive emulators. It also shows that Elkin and Peleg's (1 + ϵ, β)-spanners have an essentially optimal tradeoff between Δ, ϵ, and β, and that the sublinear additive spanners of Pettie (2009) and Chechik (2013) are not too far from optimal. To complement these lower bounds we present a new construction of (1 + ϵ, O(k ϵ)k−1)-spanners with size [EQUATION], where hk Our lower bound technique exhibits several interesting degrees of freedom in the framework of Abboud and Bodwin. By carefully exploiting these freedoms, we are able to obtain lower bounds for several related combinatorial objects. We get lower bounds on the size of (β, ϵ)-hopsets, matching Elkin and Neiman's construction (2016), and lower bounds on shortcutting sets for digraphs that preserve the transitive closure. Our lower bound simplifies Hesse's (2003) refutation of Thorup's conjecture (1992), which stated that adding a linear number of shortcuts suffices to reduce the diameter to polylogarithmic. Finally, we show matching upper and lower bounds for graph compression schemes that work for graph metrics with girth at least 2γ + 1. One consequence is that 's (2010) additive O(γ)-spanners with size [EQUATION] cannot be improved in the exponent. We propose a new semisupervised learning algorithm, referred to as patch distribution compatible semisupervised dimension reduction, for face and human gait recognition. Each image (a face image or an average human silhouette image) is first represented as a set of local patch features and it is further characterized as the corresponding patch distribution feature, which can be expressed as an image-specific Gaussian mixture model (GMM) adapted from the universal background model. Assuming that the individual components of the image-specific GMMs from all the training images reside on a submanifold, we assign a component-level prediction label matrix to each individual GMM component and introduce a new regularizer based on a set of local submanifold smoothness assumptions in our objective function. We also constrain each component-level prediction label matrix to be consistent with the image-level prediction label matrix , as well as enforce to be close to the given labels for the labeled samples. We further use a linear regression function to provide embeddings for the training data and the unseen test data. Inspired by the recent work flexible manifold embedding, we additionally integrate the regression residue in our objective function to measure the mismatch between and , such that our method can better cope with the data sampled from a nonlinear manifold. Finally, the optimal solutions of the component-level prediction label matrix , the image-level prediction label matrix , the projection matrix , and the bias term b can be simultaneously obtained. Comprehensive experiments on three benchmark face databases CMU PIE, FERET, and AR as well as the USF HumanID gait database clearly demonstrate the effectiveness of our algorithm over other state-of-the-art semisupervised dimension reduction methods. An (n, k, l) MDS code of length n, dimension k and sub-packetization l over a finite field F is a set of n symbol vectors of length l over F with the property that any k vector can recover the entire data of kl symbols. When a node fails we can recover it by downloading symbols from the surviving nodes, and the total number of symbols downloaded in th worst case is the repair bandwidth of the code. By the cut-se bound, the repair bandwidth of an (n, k, l) MDS code is at leas (n − 1)l (n − k). There are several constructions of (n, k, l) MDS codes whose repair bandwidths meet or asymptotically meet the cut-se bound. Letting r = n − k denote the number of parities Ye and Barg constructed (n, k, rn) Reed-Solomon codes the asymptotically meet the cut-set bound. Ye and Barg also constructed optimal bandwidth and optimal update (n, k, rn MDS codes. A key idea in these constructions is to expand integers in base r. We show in this paper that, when r is an integral power, w can significantly reduce the sub-packetization of the Ye-Barg constructions while achieving asymptotically optimal repair bandwidth. As an example, when r = 2m, we achieve the sub-packetization of 2m+n−1, which improves upon the sub packetization of 2mn in the Ye-Barg constructions. In general when r = sm for an integer s  2, our codes have sub packetization l = sm+n−1 = rsn−1. Specifically, in the casi r = sm, we obtain an (n, k, sm+n=1) Reed-Solomon code an an optimal update (n, k, sm+n−1) MDS code, which both have asymptotically optimal repair bandwidth. In order to obtain these results, we extend and generalize the r-ary expansion idea in the Ye-Barg constructions. Even when r is not an integral power, we can still obtain (n, k, sm+n−1) Reed-Solomon codes and optimal update (n, k, sm+n−1) MDS codes by choosing positive integers s an m such that sm  r. In this case, however, the resulting codes have bandwidth that is near-optimal rather than asymptotically optimal. In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts.