(3,3), padding = 'valid')(x) autoencoder = keras.Model(input_img, decoder_output) #autoencoder.compile(optimizer='adam', loss='binary_crossentropy') autoencoder.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001), loss = 'mean_absolute_error', metrics = ['acc']) ...
(convolutional autoencoder + k-means) outperforms established ground motion clustering algorithms.The proposed ground motion clustering algorithm can be embedded in current selection methods, which allows the selected ground motions (proportionally from each cluster) to fully represent the candidate ground ...
In this paper, we present a Deep Learning method for semi-supervised feature extraction based on Convolutional Autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation. Optical Emission Spectrometry data, that exhibit...
1. 研究背景 研究重点——以前,人们关注一段运动序列在未来的开放性预测,但现在更关注给定未来序列后中间序列是什么样的。比如,动捕系统中光学系统难以解决遮挡问题,要恢复丢失的帧或关节;或者计算机图形学的…
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
论文标题:Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning论文作者:Jiwoong Park、Minsik Lee、H. Chang、Kyuewang Lee、J. Choi论文来源:2019, ICCV论文地址:download 论文代码:download1 Introduction本文提出一个完全对称的自编码器,其中 解码器 基于Laplacian sharpening 设计;...
(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 idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event...
Abstract 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 lea...
In this paper, it is tried to increase the encryption complexity and unpredictability of the encryption scheme using different phases of chaos game representation (GCR), logistic map diffusion, and convolutional auto-encoder-based image representation. In the proposed scheme, the original image's ...