TL;DR
3D convolutional stochastic configuration networks (3DConSCN) based on fuzzy tensor product (FTP) achieves 95.27% accuracy in flotation industrial condition recognition, demonstrating its strong potential for industrial recognition applications.
Deeper modern networks outperform the older AlexNet by a wide margin on CIFAR-10, and even a relatively compact ResNet can nearly match the accuracy of a much larger VGG16 in far less time.
Unsupervised feature selection (UFS) is of great significance in image processing and pattern recognition. Nevertheless, existing model-based methods often rely on manually tuned hyperparameters, while deep learning-based methods generally require massive training data. To address these issues, this paper proposes an e...
Evaluation studies on the DSR-Net confirmed the superiority of the DSR-Net over standard unimodal deep learning baseline models and the Cross Attention-based dual stream architectures as well.
This paper evaluates a compact, from-scratch convolutional neural network (CNN) on the MNIST handwritten-digit benchmark along four simultaneous axes: classification accuracy against classical baselines, explainability faithfulness, adversarial and random-noise robustness, and feature-space separability. The CNN is imp...
Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research groups and precludes edge deployment. Additionally, generating 3D supervision without sensors still relies on slow, unreliable Structure-from-Motion, as the community lacks...
An experimental study shows that a cross-class accuracy of 0.842 in mineral construction and demolition waste classification was achieved with generative data augmentation, with significant improvement compared to a CNN trained only with original data, confirming the feasibility of edge TPUs and the effectiveness of ge...
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