In order to cope with the aforementioned drawbacks, we propose a new architecture by combining a convolutional autoencoder with convolutional neural network, which is called CAE-CNN (Convolutional AutoEncoder and Convolutional Neural Network). Specifically, motivated by the image reconstruction, we use ...
卷积自编码器(Convolutional Autoencoder)的一个实验 1、卷积自编码器(CAE)的简单介绍 卷积自编码器是自编码器方法的一种延伸,自编码器包括编码和解码,通过将输入的图像进行编码,特征映射到隐层空间,然后解码器对隐层空间的特征进行解码(重建的过程)获得输入的重建样本。自编码一般使用NN网络做编码和解码器,卷积自...
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network
Recent advancements in autoencoders and their variants have notably enhanced the detection of multi-element geochemical signatures linked to ore occurences. This research employed a convolutional autoencoder algorithm (CAE) to identify geochemical anomalies, leveraging the...
The Convolutional AutoEncoder (CAE), which has received extensive attention in recent years, has achieved good results in many high-dimensional and complex pattern recognition problems. Some studies have used the CAE to extract the hierarchical representations of images and applied it to image classifi...
We present a novel convolutional auto-encoder (CAE) for unsupervised feature learning. A stack of CAEs forms a convolutional neural network (CNN). Each CAE is trained using conventional on-line gradient descent without additional regularization terms. A max-pooling layer is essential to learn biolog...
(PCA), Convolutional Auto-Encoder (CAE), Self-Attention-based CAE (SA-CAE), Gate Recurrent Unit based Auto-Encoder (GRU-AE) and TFA-GRU-AE models; (2) flight patterns corresponding to different runways can be recognized; and (3) anomalous flights can effectively deviate from many ...
The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of ...
ConvolutionalAutoencoder(CAE) is an interesting choice, since it captures the 2D structure in image sequences during the learning process. This work uses a CAE in the anomaly detection context, by applying the reconstruction error of each frame as an anomaly score. By exploring the CAE ...
Augmentation encompasses geometric transformation and generation of synthetic images using convolutional Autoencoder (CAE). Feature extraction phase involves using image embedding and transfer-learning. Image embedding representation was obtained using CAE and then utilized by two pre-trained models: VGG19 ...