731 research outputs found
Research on Olympics-oriented Mobile Game News Ordering System
PACLIC 20 / Wuhan, China / 1-3 November, 200
A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution
Effective aggregation of temporal information of consecutive frames is the
core of achieving video super-resolution. Many scholars have utilized
structures such as sliding windows and recurrent to gather spatio-temporal
information of frames. However, although the performance of the constructed VSR
models is improving, the size of the models is also increasing, exacerbating
the demand on the equipment. Thus, to reduce the stress on the device, we
propose a novel lightweight recurrent grouping attention network. The
parameters of this model are only 0.878M, which is much lower than the current
mainstream model for studying video super-resolution. We design forward feature
extraction module and backward feature extraction module to collect temporal
information between consecutive frames from two directions. Moreover, a new
grouping mechanism is proposed to efficiently collect spatio-temporal
information of the reference frame and its neighboring frames. The attention
supplementation module is presented to further enhance the information
gathering range of the model. The feature reconstruction module aims to
aggregate information from different directions to reconstruct high-resolution
features. Experiments demonstrate that our model achieves state-of-the-art
performance on multiple datasets
Network Supplier Credit Management: Models Based on Petri Net
In current credit evaluation methods, the credit condition of the network supplier and the credit degree of each index cannot be described well, and the credit evaluation data only source of the transaction platform have much limitation. This research proposes the method of calculating the importance and the value of the credit evaluation indexes, and proposes to put credit evaluation into big data environment. This research uses the transaction process of B2C as the case, and constructs multiple attribute weighted Petri net credit index subnet (CWPSN) for realizing the credit evaluation of the network supplier, and for presenting the correlations among the evaluation results of the credit evaluation indexes, and for presenting the importance of the indexes and the credit degree of each index, and describes the cost optimization process with credit cost optimization investment process Petri net (CCOIPPN). By the case to verify the credit evaluation method based on Petri net and the cost optimization method based on Petri net. The researches have provided methods for clearly and concretely describing the process of credit evaluation and cost optimization of network supplier, and have guidance significance for similar other researches
FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging
Inverse problems are essential to imaging applications. In this paper, we
propose a model-based deep learning network, named FISTA-Net, by combining the
merits of interpretability and generality of the model-based Fast Iterative
Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and
tuning-free advantages of the data-driven neural network. By unfolding the
FISTA into a deep network, the architecture of FISTA-Net consists of multiple
gradient descent, proximal mapping, and momentum modules in cascade. Different
from FISTA, the gradient matrix in FISTA-Net can be updated during iteration
and a proximal operator network is developed for nonlinear thresholding which
can be learned through end-to-end training. Key parameters of FISTA-Net
including the gradient step size, thresholding value and momentum scalar are
tuning-free and learned from training data rather than hand-crafted. We further
impose positive and monotonous constraints on these parameters to ensure they
converge properly. The experimental results, evaluated both visually and
quantitatively, show that the FISTA-Net can optimize parameters for different
imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational
Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep
learning methods and exhibits good generalization ability over other
competitive learning-based approaches under different noise levels.Comment: 11 pages
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