Multi-label Text Categorization Using VG-RAM Weightless Neural Networks

Badué, Claudine; Pedroni, Felipe; Souza, Alberto F. De

doi:10.1109/sbrn.2008.29

Cited by 4 publications

(3 citation statements)

References 10 publications

(24 reference statements)

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“…The performed works take the adversarial concept to its limits by exploring different CNNs' vulnerability on small-scale distortions to evaluate robustness [27]. In normal approaches, rules recommend to collect homogeneous samples in order to train the best network [14]. However, in real life applications, systems are required to operate in a variable environment where everything is usually in irregular form.…”

Section: Discussionmentioning

confidence: 99%

“…On the other side, a theoretical outlook believes that multiple class models are more specific, while single class are more general. Thus, it is more complex but more powerful to use multiple single label classifiers in parallel for multi-label classification models [14]. However, this can be realized if categories are independent form each other.…”

Section: Previous Workmentioning

confidence: 99%

“…, O n to its associated address locations in the RAM representing the WNN. When all the Output sets are loaded to the RAMs in the network layers, the system is ready for operation [14]. Therefore, we have employed a set of rules to define the equation of a Translator:…”

Section: A the Translatormentioning

confidence: 99%

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Novel Preprocessors for Convolution Neural Networks

et al. 2022

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Fooling neural networks is a main concern in the process of Artificial Intelligence optimization. Character perturbation make part of a text unnoticeable for some systems, even for human observers. This research focuses the probe on a novel input preprocessing technique, which applies with the Convolutional Neural Networks for character recognition applications. Using Hand-written, Font, and Arabic Alphabet datasets, we show the CNN competitive results in classification. Then, we apply random deformation by mean of reshaping and relocation on the three testing datasets. As a reaction to change, the CNN demonstrates a confused behavior. Therefore, we design a Weightless Neural Network preprocessor that optimizes the shapes of deformed characters. Finally, we compare the impact of corrected input in improving the CNN performance. To measure the success of our preprocessor, we have applied it on well-known CNNs to compare the results between defected and rectified test datasets. Using Keras, we reconstructed LeNet-5, AlexNet and ZFNet for testing. Then, we conduct the test on the three databases to show how the recognition capability of CNNs would decline, then improve after preprocessing.INDEX TERMS Artificial neural networks, logic-based intelligence, pattern recognition, preprocessor, weightless neural networks.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%