Compositional zero-shot learning (CZSL) aims to recognize novel compositions of known attributes and objects without requiring additional training data. Recent CZSL methods based on vision-language models(e.g., CLIP) ...
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Adversarial examples pose a great threat to the application of neural network as a classifier in areas with high security requirements. Intuitively, the adversarial property of neural network is closely related to the...
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ISBN:
(纸本)9781665437585
Adversarial examples pose a great threat to the application of neural network as a classifier in areas with high security requirements. Intuitively, the adversarial property of neural network is closely related to the boundary. However, works barely give a rigorous description of it. This paper for the first time studies the boundary of ReLU NN, which separates the sample space into distinct cells, each of which belongs to the same class. During the demonstration, we discuss the algebraic property of border points upon the separating boundary and the topological difference between border points and odd ones. We derive Theorem III.7 to give a rigorous, comprehensive and concise description on the boundary of ReLU NN. Furthermore, an algorithm, based on the description, is proposed to give a standard and exact metric for the local robustness of ReLU NN. This metric gives the local adversarial property of NN and reflects some aspects of its security performance, i.e. the nimimum distortion needed to craft an adversarial example. To the best of our knowledge, this is the first work proposed to give a rigorous and example-specific metric for the local robustness of ReLU NN.
It is well known that the minimum adversarial distortion associated with a specific sample x 0 reflects the local robustness of neural networks. However, it is intractable to solve the optimization problem related to...
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ISBN:
(纸本)9781665437585
It is well known that the minimum adversarial distortion associated with a specific sample x 0 reflects the local robustness of neural networks. However, it is intractable to solve the optimization problem related to the minimum adversarial distortion for general neural network cause it is non-convex. Works have studied the lower bound or upper bound of the minimum adversarial distortion to give a robustness metrics for neural networks, such as CLEVER score and CW attack. In this paper, we provide a formal robustness guarantee to transform the robustness analysis into two sub-problems: (1), compute out the maximum of the objective function g t * (r t ); (2), generate a sequence {r t h } s.t. limn→∞ r t h is the minimum adversarial distortion for targeted attack. Based on this transformation, we propose an efficient and effective algorithm to directly estimate the instance-specific minimum adversarial distortion on the norm of the input manipulation required to change the classifier decision. Experimental results on two data-sets, MNIST and CIFAR, show that our measure of robustness is between the lower bound given by CLEVER and the upper bound by CW, indicating our approach gives a precise measure of the robustness of neural network. In addition, the powerful mathematical technique Extreme Value Estimation enables our algorithm computationally feasible for large neural networks. To the best of our knowledge, our algorithm is the first attack-independent approach to directly evaluate the minimum adversarial distortion as a robustness metric for neural network.
This paper addresses the limitations of standard chips in processing large-scale datasets by leveraging neuromorphic architectures, particularly spiking neural networks (SNNs), to simulate the pulsed signals of biolog...
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Mobile metaverses have attracted significant attention from both academia and industry, which are envisioned as the next-generation Internet, providing users with immersive and ubiquitous metaverse services through mo...
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This paper studies a long-standing problem of learning the representations of a whole graph without human supervision. The recent self-supervised learning methods train models to be invariant to the transformations (v...
ISBN:
(纸本)9781713845393
This paper studies a long-standing problem of learning the representations of a whole graph without human supervision. The recent self-supervised learning methods train models to be invariant to the transformations (views) of the inputs. However, designing these views requires the experience of human experts. Inspired by adversarial training, we propose an adversarial self-supervised learning (GASSL) framework for learning unsupervised representations of graph data without any handcrafted views. GASSL automatically generates challenging views by adding perturbations to the input and are adversarially trained with respect to the encoder. Our method optimizes the min-max problem and utilizes a gradient accumulation strategy to accelerate the training process. Experimental on ten graph classification datasets show that the proposed approach is superior to state-of-the-art self-supervised learning baselines, which are competitive with supervised models.
The Water Cherenkov Detector Array (WCDA) is one of the components of Large high Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 h per day with >98 per ce...
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Pathology symptoms of Parkinson disease (PD) are different from those of retinal diseases in the retinal layers, which are subtle. However, segmenting pathology information of PD from retinal layers automatically base...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
Pathology symptoms of Parkinson disease (PD) are different from those of retinal diseases in the retinal layers, which are subtle. However, segmenting pathology information of PD from retinal layers automatically based on optical coherence tomography (OCT) images has not been studied before. Although existing Transformer-based segmentation methods have achieved good segmentation results, they have limitations in capturing local context information. Convolutional neural networks (CNNs) can construct local context dependencies among pixels, which is complementary to Transformers. Particularly, edge information extraction is significant for accurate retinal layer segmentation, which is ignored by both Transformers and CNNs but can be captured by frequency domain learning methods. To fully leverage the advantages of Transformers, CNNs, and frequency domain learning methods, we propose a Wavelet Transformer (WaveFormer) for retinal layer segmentation based on OCT images. In the WaveFormer, we design a Wavelet Spatial Attention block to exploit the potential of frequency information. Based on these advantages, WaveFormer can be data-efficient in limited OCT images of PD. The experimental results on the OCT-PD segmentation dataset show that our WaveFormer outperforms existing Transformers and CNNs. For example, WaveFormer outperforms Swin-UNet by 3.41% of IoU.
Dear editor,Memristors have attracted a lot of attention since HP Labs first reported their memristive devices [1].They have been employed in many fields, including non-volatile memory, image processing, and neuromorp...
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Dear editor,Memristors have attracted a lot of attention since HP Labs first reported their memristive devices [1].They have been employed in many fields, including non-volatile memory, image processing, and neuromorphic computing [2–4]. In neuromorphic computing, memristors are usually used as synapses because they can store synaptic weights by their conductance [4]. Most memristors have the struc-
In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading *** provide a...
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