Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)最新文献_第2页

TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation TFCNs:用于医学图像分割的CNN-Transformer混合网络

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-07-07 DOI: 10.48550/arXiv.2207.03450

Zihan Li, Dihan Li, Cangbai Xu, Wei-Chien Wang, Qingqi Hong, Qingde Li, Jie Tian

{"title":"TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation","authors":"Zihan Li, Dihan Li, Cangbai Xu, Wei-Chien Wang, Qingqi Hong, Qingde Li, Jie Tian","doi":"10.48550/arXiv.2207.03450","DOIUrl":"https://doi.org/10.48550/arXiv.2207.03450","url":null,"abstract":"Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"66 1","pages":"781-792"},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81843600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images 光学遥感图像中显著目标检测的注意力引导网络

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-07-05 DOI: 10.48550/arXiv.2207.01755

Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, Huiyu Zhou

{"title":"Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images","authors":"Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, Huiyu Zhou","doi":"10.48550/arXiv.2207.01755","DOIUrl":"https://doi.org/10.48550/arXiv.2207.01755","url":null,"abstract":"Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing SOD methods can satisfy the detection performance for natural scene images, but they are not well adapted to RSI-SOD due to the above-mentioned image characteristics in remote sensing images. In this paper, we propose a novel Attention Guided Network (AGNet) for SOD in optical RSIs, including position enhancement stage and detail refinement stage. Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects. The detail refinement stage uses the proposed self-refinement module to progressively refine the predicted results under the guidance of attention and reverse attention. In addition, the hybrid loss is applied to supervise the training of the network, which can improve the performance of the model from three perspectives of pixel, region and statistics. Extensive experiments on two popular benchmarks demonstrate that AGNet achieves competitive performance compared to other state-of-the-art methods. The code will be available at https://github.com/NuaaYH/AGNet.","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"99 1","pages":"25-36"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81279505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Multi scale Feature Extraction and Fusion for Online Knowledge Distillation 在线知识蒸馏的多尺度特征提取与融合

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-06-16 DOI: 10.48550/arXiv.2206.08224

Panpan Zou, Yinglei Teng, Tao Niu

{"title":"Multi scale Feature Extraction and Fusion for Online Knowledge Distillation","authors":"Panpan Zou, Yinglei Teng, Tao Niu","doi":"10.48550/arXiv.2206.08224","DOIUrl":"https://doi.org/10.48550/arXiv.2206.08224","url":null,"abstract":"Online knowledge distillation conducts knowledge transfer among all student models to alleviate the reliance on pre-trained models. However, existing online methods rely heavily on the prediction distributions and neglect the further exploration of the representational knowledge. In this paper, we propose a novel Multi-scale Feature Extraction and Fusion method (MFEF) for online knowledge distillation, which comprises three key components: Multi-scale Feature Extraction, Dual-attention and Feature Fusion, towards generating more informative feature maps for distillation. The multiscale feature extraction exploiting divide-and-concatenate in channel dimension is proposed to improve the multi-scale representation ability of feature maps. To obtain more accurate information, we design a dual-attention to strengthen the important channel and spatial regions adaptively. Moreover, we aggregate and fuse the former processed feature maps via feature fusion to assist the training of student models. Extensive experiments on CIF AR-10, CIF AR-100, and CINIC-10 show that MFEF transfers more beneficial representational knowledge for distillation and outperforms alternative methods among various network architectures","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"39 3-4 1","pages":"126-138"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77867222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Attention Awareness Multiple Instance Neural Network 注意感知多实例神经网络

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-05-27 DOI: 10.48550/arXiv.2205.13750

Jingjun Yi, Beichen Zhou

引用次数: 1

A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE) 关于自组织映射(SOMs)和随机邻居嵌入(SNE)的统一观点

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-05-03 DOI: 10.48550/arXiv.2205.01492

Thibaut Kulak, Anthony Fillion, Franccois Blayo

引用次数: 0

Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks 雅可比集合改进对对抗性攻击的鲁棒性权衡

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-04-19 DOI: 10.48550/arXiv.2204.08726

Kenneth T. Co, David Martínez-Rego, Zhongyuan Hau, Emil C. Lupu

{"title":"Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks","authors":"Kenneth T. Co, David Martínez-Rego, Zhongyuan Hau, Emil C. Lupu","doi":"10.48550/arXiv.2204.08726","DOIUrl":"https://doi.org/10.48550/arXiv.2204.08726","url":null,"abstract":"Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integration into many systems also brings with it the vulnerability to test time attacks in the form of Universal Adversarial Perturbations (UAPs). UAPs are a class of perturbations that when applied to any input causes model misclassification. Although there is an ongoing effort to defend models against these adversarial attacks, it is often difficult to reconcile the trade-offs in model accuracy and robustness to adversarial attacks. Jacobian regularization has been shown to improve the robustness of models against UAPs, whilst model ensembles have been widely adopted to improve both predictive performance and model robustness. In this work, we propose a novel approach, Jacobian Ensembles-a combination of Jacobian regularization and model ensembles to significantly increase the robustness against UAPs whilst maintaining or improving model accuracy. Our results show that Jacobian Ensembles achieves previously unseen levels of accuracy and robustness, greatly improving over previous methods that tend to skew towards only either accuracy or robustness.","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"65 1","pages":"680-691"},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79248862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Stream-based Active Learning with Verification Latency in Non-stationary Environments 非平稳环境下具有验证延迟的基于流的主动学习

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-04-14 DOI: 10.1007/978-3-031-15937-4_22

Andrea Castellani, Sebastian Schmitt, Barbara Hammer

引用次数: 5

A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets 一种训练不同类型的推文健康提及分类语言模型的新方法

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-04-13 DOI: 10.48550/arXiv.2204.06337

Pervaiz Iqbal Khan, Imran Razzak, A. Dengel, Sheraz Ahmed

{"title":"A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets","authors":"Pervaiz Iqbal Khan, Imran Razzak, A. Dengel, Sheraz Ahmed","doi":"10.48550/arXiv.2204.06337","DOIUrl":"https://doi.org/10.48550/arXiv.2204.06337","url":null,"abstract":"Health mention classification deals with the disease detection in a given text containing disease words. However, non-health and figurative use of disease words adds challenges to the task. Recently, adversarial training acting as a means of regularization has gained popularity in many NLP tasks. In this paper, we propose a novel approach to train language models for health mention classification of tweets that involves adversarial training. We generate adversarial examples by adding perturbation to the representations of transformer models for tweet examples at various levels using Gaussian noise. Further, we employ contrastive loss as an additional objective function. We evaluate the proposed method on the PHM2017 dataset extended version. Results show that our proposed approach improves the performance of classifier significantly over the baseline methods. Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance. Finally, adding noise towards the final layers performs better than the middle layers noise addition.","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"157 1","pages":"136-147"},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73694540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks 深度网络中对抗性示例与类流形的接近性研究

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-04-12 DOI: 10.48550/arXiv.2204.05764

Stefan Pócos, Iveta Becková, I. Farkaš

{"title":"Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks","authors":"Stefan Pócos, Iveta Becková, I. Farkaš","doi":"10.48550/arXiv.2204.05764","DOIUrl":"https://doi.org/10.48550/arXiv.2204.05764","url":null,"abstract":"Deep neural networks achieve remarkable performance in multiple fields. However, after proper training they suffer from an inherent vulnerability against adversarial examples (AEs). In this work we shed light on inner representations of the AEs by analysing their activations on the hidden layers. We test various types of AEs, each crafted using a specific norm constraint, which affects their visual appearance and eventually their behavior in the trained networks. Our results in image classification tasks (MNIST and CIFAR-10) reveal qualitative differences between the individual types of AEs, when comparing their proximity to the class-specific manifolds on the inner representations. We propose two methods that can be used to compare the distances to class-specific manifolds, regardless of the changing dimensions throughout the network. Using these methods, we consistently confirm that some of the adversarials do not necessarily leave the proximity of the manifold of the correct class, not even in the last hidden layer of the neural network. Next, using UMAP visualisation technique, we project the class activations to 2D space. The results indicate that the activations of the individual AEs are entangled with the activations of the test set. This, however, does not hold for a group of crafted inputs called the rubbish class. We also confirm the entanglement of adversarials with the test set numerically using the soft nearest neighbour loss.","PeriodicalId":93416,"journal":{"name":"Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society)","volume":"17 1","pages":"645-656"},"PeriodicalIF":0.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74886642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning Trajectories of Hamiltonian Systems with Neural Networks 神经网络哈密顿系统的学习轨迹

Artificial neural networks, ICANN : international conference ... proceedings. International Conference on Artificial Neural Networks (European Neural Network Society) Pub Date : 2022-04-11 DOI: 10.48550/arXiv.2204.05077

Katsiaryna Haitsiukevich, A. Ilin

引用次数: 2