Results for convolutional networks
Gabor Convolutional Networks
... Gabor Convolutional Networks Shangzhen Luan, Chen Chen, Member, IEEE, Baochang Zhang*, Member, IEEE, Jungong Han*, Member, IEEE, and Jianzhuang Liu, Senior Member, IEEE Abstract—In steerable filters, a ...
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Measuring (in)variances in Convolutional Networks
... the networks are not learning to be invariant in all their outputs, just in those corresponding to the expected ...1 results in a high variance for all classes, which indicates that the network has ...
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Convolutional Networks for Stock Trading
... To remedy this, I tried a new network structure which didn’t employ pooling until several cycles of convolution– ReLU. Concretely, I used three blocks of conv–ReLU, fol- lowed by two blocks of conv–ReLU–pool, followed by ...
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Siamese Convolutional Networks for Cognate Identification
... The results for Mayan family suggests that the CharCNN can be used with small datasets for a closely related language family. We believe that this is an important result due to the abundance of small number of ...
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2. Spectral Aliasing in Convolutional Networks
... Our model delays the removal of high frequencies emerg- ing from non-linear operations until the subsequent sub- sampling operation. Consequently, our formulation main- tains high-frequencies throughout the trainable ...
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Graph Convolutional Networks for Text Classification
... applied convolutional neural networks (convolu- tion on regular grid, ...graph convolutional neural networks (convolution on non-grid, ...graph convolutional networks for text ...
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Dense convolutional networks for efficient video analysis
... attenuation is 0.0005, and the minus-sized is 32. All the training was done on a tesla P100 GPU. Test time inference: In order to improve the accuracy, many start-of-the-art algorithms prefer to run some post- ...
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Convolutional Kernel Networks
... 2 results for CKN-GM, defined in the previous section, without exploiting color information, and CKN-PM when working on raw RGB patches whose mean color is ...
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Calibration of Convolutional Neural Networks
... final results are achieved using an hold-out test set, it is separate from the training set and the validation ...of convolutional layers in ...best results are in ...
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Pansharpening by convolutional neural networks
... Numerical results speak very clearly in favor of the proposed solution. Let us focus, for the time being, on the WorldView-2 dataset, as in Table 10. Under all full-reference metrics, CNN-based pansharpening ...
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Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
... input results in a bounded perturbation in the output – indicating the stability of the CNN in recovering the underlying ...recurrent networks (Bengio et ...
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Resiliency in Deep Convolutional Neural Networks
... gives results that are very close to ADAM at almost 70% of the ...better results when weights have been dropped above ...get results very close to Rprop at a faster ...
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1 Convolutional Neural Networks Architectures
... These networks use skip connections to allow inputs and gradients to propagate faster throughout the network (either forward or ...Residual networks were state of the art for image recognition ...
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Convolutional Neural Networks for Accent Classification
... In the case of binary classification a confusion matrix should have two rows and two columns. However, in this thesis a multiclass classification with four classes is used. Therefore, the structure of the confusion ...
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Labeling Paths with Convolutional Neural Networks
... Our initial model utilized two convolutional layers a hid- den layer and a 16x9 output layer. When initially attempting to train the model on the dataset, we had trouble approaching any reasonable accuracy. ...
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Pre-training in convolutional neural networks
... activation. Results on digit classication and object recognition tasks show that this method can give compatible or even better results than by-then-state-of-art ...
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Localization Using Convolutional Neural Networks
... interesting results since VGG16 and VGG19 were comparable despite the deeper VGG19 ...These results are to be expected due to the symmetry of the building that was ...
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Convolutional Neural Networks for Sentence Classification
... excellent results on multiple benchmarks, suggesting that the pre-trained vectors are ‘universal’ feature ex- tractors that can be utilized for various classifica- tion ...fine-tuning results in further ...
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Convolutional neural networks for malware classification
... 7.1. FUTURE WORK 7.1 Future Work Even that both approaches have been successfully applied, there is still a huge margin of improvement. From now on, consider that the CNN used to classify malware based on their ...
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Representation Learning With Convolutional Neural Networks
... ber representations need to be not only separable but also discriminative. Center loss was introduced which adds a cluster-based loss term to the cross-entropy loss to ensure the learned representations have both compact ...
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