In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and accuracy. In our proposed visual concept recognition system, we first discuss various important ...
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The above results confirm that the combination of remote sensing images and convolutional neural network technology can perform simple ecological planning of a city's urban and rural areas, which proves that the method proposed in this experiment has practicality....
Our proposed RL-DITR as a model-based RL consists of two components: a patient model to characterize the diabetes progressive state by learning the environment’s dynamics and a policy model for management of diabetes by planning with respect to the learned model. Specifically, the patient model ...
& Shen, Y. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 35, 3329–3338 (2019). Article CAS PubMed PubMed Central Google Scholar Zhao, Q., Zhao, H., Zheng, K. & Wang, J. HyperAttentionDTI:...
We test the possibility of using a convolutional neural network to infer the inclination angle of a black holedirectlyfrom the incomplete image of the black hole's shadow in theuv-plane. To this end, we develop a proof-of-concept network and use it to explicitly find how the error depends...
WReN-Bongard—A variant ofWReNthat was originally designed to encourage reasoning in theRaven-style Progressive Matrices(RPMs). CNN-Baseline—A convolutional neural network (CNN) baseline, which casts the task into a conventional binary image classification problem. ...
[4] to compute the matching costs. They trained a convolutional neural network (CNN) to predict whether two image patches match or not. In recent years, many confidence measures aiming at detecting unreliable disparity assignments, proved to be very effective cues when combined with state-of-the...
When a concept whitening module is added to a convolutional neural network, the latent space is whitened (that is, decorrelated and normalized) and the axes of the latent space are aligned with known concepts of interest. By experiment, we show that CW can provide us with a much clearer ...
The effect of CRP over LRP and other attribution methods is an increase in detail of the obtained explanations. Given a typical image classification convolutional neural network (CNN), one may assume the computation of three-dimensional latent tensors, where the first two axes span the application...