Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

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A Focally Discriminative Loss for Unsupervised Domain Adaptationised domain adaptation (UDA), where both domains follow different distributions, and the labels from source domain are merely available. However, MMD and its class-wise variants possibly ignore the intra-class compactness, thus canceling out discriminability of feature representation. In this paper,

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Automatic Drum Transcription with Label Augmentation Using Convolutional Neural Networks The successful transcription of drum instruments is a key step in the analysis of drum music. Existing systems use the target drum instruments as a separate training objective, which faces the problems of over-fitting and limited performance improvement. To solve the above limitations, this paper p

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Adaptive Curriculum Learning for Semi-supervised Segmentation of 3D CT-Scanses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of gener

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Genetic Algorithm and Distinctiveness Pruning in the Shallow Networks for VehicleXf real data often brings up privacy and data security issues. This paper aims to build a shallow neural network model for the pre-trained synthetic feature dataset, VehicleX. Using genetic algorithm to reduce the dimensional complexity by randomly selecting a subset of features from before training.

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Stack Multiple Shallow Autoencoders into a Strong One: A New Reconstruction-Based Method to Detect A input from high-level features extracted from the samples. The underlying assumption of these methods is that a deep model trained on normal data would produce higher reconstruction error for abnormal input. But this underlying assumption is not always valid. Because the neural networks have a stro

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