The method to improve healthcare outcomes and to treat all diseases, from acute bacterial sinusitis and adenocarcinoma of the pancreas, and all the way to atopic dermatitis, is through medication effectiveness assessm...
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It is crucial for autonomous vehicles to make safe and effective decisions in real-time dynamic road environments through decision-making systems. Traditional rulebased decision-making methods struggle to handle compl...
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A crucial problem in natural language processing is language identification, which has applications in speech recognition, translation services, and multilingual content. The five main Indian languages that are the su...
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Currently, the field of individual identification utilizing coded modulation visual evoked potentials (cVEP) is gaining significant attention. However, existing methods face challenges due to the EEG signals' low ...
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Quantum-inspired models have demonstrated superior performance in many downstream language tasks, such as question answering and sentiment analysis. However, recent models primarily focus on embedding and measurement ...
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In the internet era, almost every business entity is trying to have its digital footprint in digital media and other social media platforms. For these entities, word of mouse is also very important. Particularly, this...
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In the field of image segmentation, U-Net and Fully Convolutional Networks (FCNs) have achieved great success, but these models have two limitations, their optimal depth is unknown a priori, and requires extensive sch...
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Supervised contrastive representation learning has been shown to be effective in various transfer learning scenarios. However, while asymmetric non-contrastive learning (ANCL) often outperforms its contrastive learnin...
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Supervised contrastive representation learning has been shown to be effective in various transfer learning scenarios. However, while asymmetric non-contrastive learning (ANCL) often outperforms its contrastive learning counterpart in self-supervised representation learning, the extension of ANCL to supervised scenarios is less explored. To bridge the gap, we study ANCL for supervised representation learning, coined SUPSIAM and SUPBYOL, leveraging labels in ANCL to achieve better representations. The proposed supervised ANCL framework improves representation learning while avoiding collapse. Our analysis reveals that providing supervision to ANCL reduces intra-class variance, and the contribution of supervision should be adjusted to achieve the best performance. Experiments demonstrate the superiority of supervised ANCL across various datasets and tasks. The code is available at: https://***/JH-Oh-23/Sup-ANCL. Copyright 2024 by the author(s)
Online reviews are a valuable source for understanding tourist satisfaction and their emotional tendencies towards attractions. However, there is a need to improve the quality screening of reviews before conducting se...
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Lace texture, as a manually designed texture image, needs to possess a series of essential aesthetic characteristics, such as periodicity, symmetry, and blank-leaving in artistic design creation. It requires human des...
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