The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS...
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Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In ...
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In semi-supervised medical image segmentation, two main challenges arise. First, the quality of pseudo-labels generated by segmentation networks in data-limited scenarios is often poor, reducing segmentation accuracy....
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With the development of distributed machine learning and federated learning, the solution to the data island problem is promoted. People use computer clusters to train machine learning models on data distributed in di...
With the development of distributed machine learning and federated learning, the solution to the data island problem is promoted. People use computer clusters to train machine learning models on data distributed in different regions. In the early stage of research, researchers usually assume that the data sets of each node are independent identically distribution (IID), but this is a strong assumption, which is challenging to meet in practical applications. Therefore, research on non-IID has become a hot spot in recent years. However, there is no uniform standard for designing and evaluating non-IID scenarios. This paper proposes a Frechet distance-independent non-IID distribution dataset metric MDFD. And we conducted experiments on different types of distributed machine-learning methods in different non-IID scenarios to verify the effectiveness of MDFD.
High-temperature oxidation is a common failure in high-temperature environments,which widely occur in aircraft engines and aerospace thrusters;as a result,the development of anti-high-temperature oxidation materials h...
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High-temperature oxidation is a common failure in high-temperature environments,which widely occur in aircraft engines and aerospace thrusters;as a result,the development of anti-high-temperature oxidation materials has been ***-based alloys are a common high-temperature material;however,they are too ***-entropy alloys are alternatives for the anti-oxidation property at high temperatures because of their special structure and *** recent achievements of high-temperature oxidation are reviewed in this *** high-temperature oxidation environment,temperature,phase structure,alloy elements,and preparation methods of high-entropy alloys are *** reason why high-entropy alloys have anti-oxidation ability at high temperatures is *** research,material selection,and application prospects of high-temperature oxidation are introduced.
Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations ...
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Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perfo...
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The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data s...
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Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and sha...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet.
The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as "learning repr...
ISBN:
(纸本)9798331314385
The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as "learning representations (or features) of the data that make it easier to extract useful information when building classifiers or other predictors" [4]. In this paper, we propose a novel Denoising Model for Representation Learning (DenoiseRep) to improve feature discrimination with joint feature extraction and denoising. DenoiseRep views each embedding layer in a backbone as a denoising layer, processing the cascaded embedding layers as if we are recursively denoise features step-by-step. This unifies the frameworks of feature extraction and denoising, where the former progressively embeds features from low-level to high-level, and the latter recursively denoises features step-by-step. After that, DenoiseRep fus es the parameters of feature extraction and denoising layers, and theoretically demonstrates its equivalence before and after the fusion, thus making feature denoising computation-free. DenoiseRep is a label-free algorithm that incrementally improves features but also complementary to the label if available. Experimental results on various discriminative vision tasks, including re-identification (Market-1501, DukeMTMC-reID, MSMT17, CUHK-03, vehicleID), image classification (ImageNet, UB200, Oxford-Pet, Flowers), object detection (COCO), image segmentation (ADE20K) show stability and impressive improvements. We also validate its effectiveness on the CNN (ResNet) and Transformer (ViT, Swin, Vmamda) architectures. Code is available at https://***/wangguanan/DenoiseRep.
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