Robust deep convolutional autoencoder. Feb 1, 2025 · The method includes: (i) using wavelet transform to represent the original noise signal and designing a soft and hard denoising module for dataset denoising; (ii) deep residual convolutional denoising variational autoencoder (VAE) module performs representation learning with a VAE and deep residual convolutional neural networks, enabling richer Mar 1, 2026 · To address the above challenges, this paper develops a hybrid robust convolutional autoencoder (HRCAE) for unsupervised anomaly detection of machine tools under noises. Besides, we proposed a metric to guide the model training based on the spatial scale of abnormal regions. The framework trains anomaly transformer models on normal data refined by complementary noise filters, then applies K-means clustering to the resulting multidimensional anomaly scores, separating Nov 1, 2020 · We have used an automatic unsupervised technique to extract waveform signals from continuous microseismic data. Mar 15, 2025 · This dual-training approach makes the autoencoder to be robust against potential manipulations or anomalies in data, thereby enhancing security and reliability, especially in sensitive applications like medical imaging and fraud detection. Mar 15, 2025 · This paper provides a comprehensive review of autoencoder architectures, from their inception and fundamental concepts to advanced implementations such as adversarial autoencoders, Mar 1, 2026 · Our approach adopted a stacked convolutional denoising autoencoder (SCDAE) to extract robust features and decreased the level of sensitivity to partially corrupted data, that is, input data that 6 days ago · To accurately capture the complex morphological features of deep reactive ion etching (DRIE) profiles from SEM images, we propose a physics-constrained variational level-set autoencoder (VLSet-AE 4 days ago · The approach combines the robust deep channel feature extraction proficiency of the feed-forward denoising convolutional neural network (DnCNN) with the effective key feature acquisition capacity of a lightweight attention mechanism (LAM). In this work, we propose an ML topology for performing efficient and robust classification in high-dimensional and noisy input data images. Dec 18, 2025 · Specifically, this method enhances the autoencoder to learn robust normality by adding noise to the input images. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Second, the convolutional autoencoder (CAE) is used to extract the significant scalogram features related to the waveform signals and discard the rest. Aug 4, 2017 · Herein, we demonstrate novel extensions to deep autoencoders which not only maintain a deep autoencoders' ability to discover high quality, non-linear features but can also eliminate outliers and noise without access to any clean training data. aqkswvl zty ewhfw ehvzg cblcap gvbplf zawhq jjdkjt zxpuraq fqejll