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Longitudinal Dependence of Heavy Ion Composition in the 2021 October 28 Ground Level Enhancement Event
Published 2025-01-01Get full text
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Are Stars Really Ingesting Their Planets? Examining an Alternative Explanation
Published 2025-01-01Get full text
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Investigating the Electron-capture Supernova Candidate AT 2019abn with JWST Spectroscopy
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Once a Triple, Not Always a Triple: The Evolution of Hierarchical Triples That Yield Merged Inner Binaries
Published 2024-01-01Get full text
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JWST Near-infrared Spectroscopy of High-albedo Jupiter Trojans: A New Surface Type in the Trojan Belt
Published 2025-01-01Get full text
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Context-Aware and Locality-Constrained Coding for Image Categorization
Published 2014-01-01“…Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. …”
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Membership inference attacks against transfer learning for generalized model
Published 2021-10-01“…For the problem of poor performance of exciting membership inference attack (MIA) when facing the transfer learning model that is generalized, the MIA for the transfer learning model that is generalized was first systematically studied, the anomaly detection was designed to obtain vulnerable data samples, and MIA was carried out against individual samples.Finally, the proposed method was tested on four image data sets, which shows that the proposed MIA has great attack performance.For example, on the Flowers102 classifier migrated from VGG16 (pretraining with Caltech101), the proposed MIA achieves 83.15% precision, which reveals that in the environment of transfer learning, even without access to the teacher model, the MIA for the teacher model can be achieved by visiting the student model.…”
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CRF combined with ShapeBM shape priors for image labeling
Published 2017-01-01“…Conditional random field (CRF) is a powerful model for image labeling,it is particularly well-suited to model local interactions among adjacent regions (e.g.superpixels).However,CRF doesn't consider the global constraint of objects.The overall shape of the object is used as a global constraint,the ShapeBM can be taken advantage of modeling the global shape of object,and then a new labeling model that combined the above two types of models was presented.The combination of CRF and ShapeBM was based on the superpixels,through the pooling technology was wed to establish the corresponding relationship between the CRF superpixel layer and the ShapeBM input layer.It enhanced the effectiveness of the combination of CRF and ShapeBM and improved the accuracy of the labeling.The experiments on the Penn-Fudan Pedestrians dataset and Caltech-UCSD Birds 200 dataset demonstrate that the model is more effective and efficient than others.…”
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Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization
Published 2018-01-01“…Our experiments were conducted on Office and Caltech10 datasets and verify the effectiveness of the model we proposed.…”
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Towards large-scale quantum optimization solvers with few qubits
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Free-space optical spiking neural network.
Published 2024-01-01“…The OSCNN was rigorously tested on benchmark datasets, including MNIST, ETH80, and Caltech, demonstrating competitive classification accuracy. …”
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CNN-Based Time Series Decomposition Model for Video Prediction
Published 2024-01-01“…To assess the performance of the proposed technique, experiments were conducted using the moving MNIST, KTH, and KITTI-Caltech benchmark datasets. In the experiments on moving MNIST, despite a reduction of approximately 55% in the number of parameters and 37% in computational cost, the proposed method improved accuracy by up to 7% compared to the previous approach.…”
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