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Robust Learning with Implicit Residual Networks [PDF]
Machine Learning and Knowledge Extraction, 2020 In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed points of the appropriately chosen nonlinear transformations.
Viktor Reshniak, Clayton G. Webster
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Residual learning: A new paradigm to improve deep learning-based segmentation of the left ventricle in magnetic resonance imaging cardiac images [PDF]
Journal of Medical Signals and Sensors, 2021 Background: Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate ...
Maral Zarvani +3 more
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Deep Residual Learning for Nonlinear Regression. [PDF]
Entropy (Basel), 2020 Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block.
Chen D, Hu F, Nian G, Yang T.
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Structure-Preserving Histopathological Stain Normalization via Attention-Guided Residual Learning [PDF]
BioengineeringStaining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms.
Nuwan Madusanka +3 more
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Residue–Residue Interaction Prediction via Stacked Meta-Learning [PDF]
International Journal of Molecular Sciences, 2021 Protein–protein interactions (PPIs) are the basis of most biological functions determined by residue–residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design.
Chen, Kuan-Hsi, Hu, Yuh-Jyh
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Knowledge-based Residual Learning [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it.
Guanjie Zheng +6 more
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Image Super-Resolution Based on Residual Attention and Multi-Scale Feature Fusion
IEEE Access, 2023 At present, deep residual network has been widely used in image super-resolution and proved to be able to achieve good reconstruction results. However, the existing super-resolution algorithms based on deep residual network have the problems of ...
Qiqi Kou +4 more
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RHN: A Residual Holistic Neural Network for Edge Detection
IEEE Access, 2021 Edge detection plays a very important role in many image processing and computer vision applications. Use of deep convolutional neural networks (DCNNs) has significantly advanced the performance of image edge detection techniques.
Abdullah Al-Amaren +2 more
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Collaborative Residual Metric Learning
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 Accepted by SIGIR ...
Tianjun Wei +2 more
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Co-Inference Discriminative Tracking Through Multi-Task Siamese Network
IEEE Access, 2021 In essence, visual tracking is a matching problem without any prior information about a class-agnostic object. By leveraging large scale off-line training data, recent trackers based on Siamese networks usually expect to pre-learn underlying similarity ...
Yan Chen, Jixiang Du, Bineng Zhong
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