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Using Applicability to Quantifying Octave Resonance in Deep Neural Networks.

, , and . ICONIP (5), volume 1333 of Communications in Computer and Information Science, page 229-237. Springer, (2020)

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Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images., , , , , , , , , and 1 other author(s). ICDM Workshops, page 763-769. IEEE, (2018)Context-Aware Design of Cyber-Physical Human Systems (CPHS)., , , , , , , , , and 1 other author(s). COMSNETS, page 322-329. IEEE, (2020)Using Applicability to Quantifying Octave Resonance in Deep Neural Networks., , and . ICONIP (5), volume 1333 of Communications in Computer and Information Science, page 229-237. Springer, (2020)Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images., , , , , , , , , and 1 other author(s). CoRR, (2019)GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks., and . ICPR, page 8384-8391. IEEE, (2020)Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)., , , and . IJCNN, page 1-9. IEEE, (2019)PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters., , and . ICADL, volume 11853 of Lecture Notes in Computer Science, page 3-15. Springer, (2019)SimilarityGAN: Using Similarity to Loosen Structural Constraints in Generative Adversarial Models., and . DICTA, page 1-8. IEEE, (2021)