The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily...
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Generative artificial intelligence is an artificial intelligence technology based on deep learning whose core lies in leveraging computer algorithms and training data to generate new, practically valuable content, enc...
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We plan to develop a specialized training system to enhance the competitive skills of players in the first-person shooter game "Valorant", aiming to improve their abilities and tactical understanding within ...
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The electrical fire monitoring mode is mostly set to one-way mode, which can achieve the expected monitoring task, but lacks stability and reliability, and it is difficult to achieve coordination and positioning monit...
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Based on the CiteSpace bibliometric method, 273 research papers from the Web of science Core Database from 2003 to 2023 were analyzed. This study delves into external characteristics such as publication volume, partic...
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Digitalization of education is one of the major strategies for my country’s current education reform and development, and market-oriented online education companies are one of the frontier practitioners in promoting ...
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This research concentrates on how fuzzy logic used in the realm of precision agriculture can make information more available to farmers, with emphasis on plant nutrition and fertilization. Our focus, in this regard, h...
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Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain. While the source model is a key avenue for acquiring target pseudola...
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Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain. While the source model is a key avenue for acquiring target pseudolabels, the generated pseudolabels may exhibit source bias. In the conventional SFDA pipeline, a large data (e.g. ImageNet) pre-trained feature extractor is used to initialize the source model at the start of source training, and subsequently discarded. Despite having diverse features important for generalization, the pre-trained feature extractor can overfit to the source data distribution during source training and forget relevant target domain knowledge. Rather than discarding this valuable knowledge, we introduce an integrated framework to incorporate pre-trained networks into the target adaptation process. The proposed framework is flexible and allows us to plug modern pre-trained networks into the adaptation process to leverage their stronger representation learning capabilities. For adaptation, we propose the Co-learn algorithm to improve target pseudolabel quality collaboratively through the source model and a pre-trained feature extractor. Building on the recent success of the vision-language model CLIP in zero-shot image recognition, we present an extension Co-learn++ to further incorporate CLIP's zero-shot classification decisions. We evaluate on 4 benchmark datasets and include more challenging scenarios such as open-set, partial-set and open-partial SFDA. Experimental results demonstrate that our proposed strategy improves adaptation performance and can be successfully integrated with existing SFDA methods.
This paper discusses the core features of computer database technology and elaborates on its practical strategies in the field of information management. It aims to optimize information management processes, enhance t...
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