Because of the unclear boundaries and different shapes and sizes of breast masses, the accuracy of using traditional computer-aided diagnosis systems is low and it is difficult to meet the clinical requirements of phy...
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Reconstructing HDR images from a single LDR image with noise and saturated regions is a highly challenging problem. Recent approaches have utilized cascaded network structures to address this challenge in image space....
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Image segmentation is an important problem in the field of image processing. Traditional multi-threshold image segmentation faces the problems of inefficiency and lack of real-time performance. In order to improve the...
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The utilization of convolutional neural network (CNN) inference models has gained widespread adoption across various domains;however, it raises concerns regarding user privacy and security due to the requirement of pl...
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In the direction of VR/AR Human-Machine Interaction, natural and simple dynamic gesture recognition research has attracted much attention. For the sake of improve the accuracy of dynamic gesture recognition in Human-M...
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The present paper proposes a double-channel short text classification model with fused enhanced features to tackle the issues of feature sparsity, incomplete feature extraction, and important information loss in short...
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Generative adversarial networks(GANs) have drawn enormous attention due to their simple yet efective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolutio...
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Generative adversarial networks(GANs) have drawn enormous attention due to their simple yet efective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolution(e.g., 1024 × 1024) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent studies show emerging interest to take advantage of pre-trained GAN models by exploiting the well-disentangled latent space and the learned GAN priors. In this study, we briefly review recent progress on leveraging pre-trained large-scale GAN models from three aspects, i.e.,(1) the training of large-scale generative adversarial networks,(2) exploring and understanding the pre-trained GAN models, and(3) leveraging these models for subsequent tasks like image restoration and editing.
Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled d...
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Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships i...
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Emotion analysis is divided into emotion detection, where the system detects if there is an emotional state, and emotion recognition where the system identifies the label of the emotion. In this paper, we provide a mu...
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