In this study, we employ QCNN with activation functions based on Bessel-type functions with trainable parameters, for performing classification tasks. Our experimental results demonstrate that this activation function outperforms the traditional ReLU activation function. Throughout our simulations, we ...
SIBI Sign Language Recognition Using Convolutional Neural Network Combined with Transfer Learning and non-trainable Parameters Author links open overlay panelSuharjito a b, Narada Thiracitta a, Herman Gunawan aShow more Add to Mendeley Share Cite https://doi.org/10.1016/j.procs.2020.12.011Get ...
Architecture optimization, which is a technique for finding an efficient neural network that meets certain requirements, generally reduces to a set of multiple-choice selection problems among alternative sub-structures or parameters. The discrete nature of the selection problem, however, makes this optimi...
The evaluation results in this paper present some empirical proof of improvement in term of network generalization performance. Moreover, our experiments strengthen our initial hypothesis regarding the advantages obtained from using our proposed method. Finally, the behavior of the trained parameters along...
A form of a governing differential equation characterizing a plant may include parameters and their derivatives of various orders as variables combined in linear and nonlinear terms. Classical control theory, and a method such as a Fourier transform of governing equations, may provide 8a form of a...
The last contribution is to show that, in many cases, using a trainable activation is equivalent to using a deeper neural network model with additional constraints on the parameters. The work is so organized as follows: in Section 2 we propose a possible activation function taxonomy, ...
Training logs, both in text and Tensorboard formats as well as the trained network parameters, will be written inLOG_DIR(e.g.ckpt_files_OCID). The fileCONFIGcontains the network configuration e.g.grasp_det_seg/config/defaults/det_seg_OCID.ini, andDATA_DIRpoints to the previously downloaded...
As a result, all parameters of our richer CRF model can be jointly learned with a CNN classifier during the end-to-end training of the entire network. We find significant improvement in the results with the introduction of these trainable higher order potentials....
All the classifiers are trained with default parameters. In this experiment, training set and testing set are similar to the previous experiment, and number of superpixel is 8000 for each image. Experiment results are showed in Table 1. Table 1 Different learning algorithms supplied with HLFs ...
(CNNs) and transformers, have demonstrated considerable success in a multitude of applications, including computer vision [1], [2], natural language processing [3], and signal processing [4], [5], [6]. However, these models often possess a considerable number of parameters, elevated energy ...