Diffusion models have emerged as powerful tools for image generation, offering flexibility in generating images conditioned on specific classes or properties. Unlike GANs, diffusion models can be conditioned during tr...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Diffusion models have emerged as powerful tools for image generation, offering flexibility in generating images conditioned on specific classes or properties. Unlike GANs, diffusion models can be conditioned during training with relative ease. However, adapting pre-trained diffusion models to generate images from new, unlabeled data remains a significant challenge. The ADM-G approach addresses this by guiding diffusion models to generate images from a given class, but it often produces results of lower quality compared to models originally trained with class-specific conditioning. For instance, the ADM-G-guided model achieves an FID score nearly three times worse than that of a class-conditioned guidance. We identify that this performance gap arises partly because ADM-G provides minimal guidance during the final stages of the denoising process. To overcome this limitation, we introduce GeoGuide, a novel guidance method that improves the model's trajectory alignment with the data manifold. GeoGuide refines the backward denoising process by applying normalized adjustments to the model's output. Experimental results show that GeoGuide significantly outperforms ADM-G in both FID scores and the visual quality of the generated images.
In this study, we aim to enhance the capability of modern techniques used in predicting Bitcoin price movement. While the current methods can only predict the direction of price movement, our objective is to predict t...
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Optimal interpretation of altimetry requires radial positioning of the satellite to accuracy approaching that of the instrumentation. For ERS–1 this is currently unrealistic due to uncertainties in the gravitational ...
Optimal interpretation of altimetry requires radial positioning of the satellite to accuracy approaching that of the instrumentation. For ERS–1 this is currently unrealistic due to uncertainties in the gravitational and surface force modelling but close to realisation for the higher orbit of TOPEX/POSEIDON. From August 1992 the altimeters on board ERS–1 and TOPEX/POSEIDON will be in simultaneous operation, enabling exploitation of the merged datasets and refinement of the less accurate ERS–1 radial ephemeris to accuracies hopefully not far short of those of TOPEX/POSEIDON. Within this study simulations have been undertaken to examine a procedure to refine the less precise ERS–1 radial ephemeris by use of single and dual satellite crossovers with TOPEX/POSEIDON. The procedure recovers a time series expansion of the radial error through least–squares minimisation of crossover residuals. Simulations consider refinement of ERS–1 by itself and of ERS–1 and TOPEX/POSEIDON simultaneously. Deficiencies leading to singularities in the least–square refinement are identified. The simulations confirm that the methodology has the capability of refining ERS–1 orbits to accuracies nearly comparable to TOPEX/POSEIDON.
Minimally invasive surgery has seen significant advancements with the introduction of robotic systems, which are highly desirable due to their ability to enhance treatment scalability and precision. This study aims to...
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In developing countries, the well-being of the elderly is not given enough attention, especially in terms of nutrition. This is a vital point since the meal which the elderly eat typically reflects their health. This ...
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In the current context, when the tourist offers are many and with various facilities, hotels and travel agencies must bring something new in their offer to attract customers and propose them opportunities to spend the...
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In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is...
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In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.
Privacy by design principles is an established standard guiding the design and development of privacy-aware systems. Privacy engineering acts as a role to close the gap between the privacy policy and the realization o...
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This paper uses semantic web and ontology techniques to predict the risk analysis of patients with diabetes mellitus. The data is collected from patients through personal interaction and by accessing their previous me...
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The paper investigates the relative expressiveness of two logic-based languages for reasoning over streams, namely LARS Programs - the language of the Logic-based framework for Analytic Reasoning over Streams called L...
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