unsupervised segmentation method of remote sensing images that rely more on features and prior knowledge, an unsupervised remote sensing image segmentation method combined with the atrous convolution autoencoder and the bidirectional long- and short-time memory autoencoder autoencoder (BiLSTM) network is...
stacked_capsule_autoencoders standalone_self_attention_in_vision_models star_cfq state_of_sparsity stochastic_polyak stochastic_to_deterministic storm_optimizer strategic_exploration streetview_contrails_dataset structformer structured_multihashing student_mentor_dataset_cleaning subclass_distillation sufficient_in...
U-Net [19] uses a structure similar to that of an autoencoder to implement a skip connection between the high-resolution and low-resolution feature maps to achieve multiscale feature fusion. An FPN [20] fuses the features of the previous layer after performing upsampling with the features of...
While there are many options for getting useful information from graphs, in this study we use a transformer-based autoencoder (TransformerAE) introduced by the CFAGO paper. The TransformerAE takes the raw adjacency matrix of PPI network (minmax-normalized weighted vectors) and the protein attribute...
ERGO2.037,54from the webserver (https://tcr2.cs.biu.ac.il/home) selecting the versions that did not include the McPAS dataset60in the training set. Both the Long Short-Term Memory (LSTM) and the AutoEncoders (AE) based were considered. ...
Reconstruction-based methods define anomalies by meticulously analyzing deviations in domain mapping and the data reconstruction processes. Huang et al. [5] advanced a memory residual regression autoencoder to improve the detection accuracy; it used the reconstruction errors and surprisal values to indicat...
The framework consists of an AutoEncoder and a deep neural network(DNN), where AutoEncoder is applied to detect zero-day intrusion, and DNN is employed for classifying known attack, respectively. In particular, we have introduced aggregation mechanism based on DBSCAN algorithm and voting system ...
In contrast to AST and M3DM, EasyNet [21] without utilizing pre-trained models and memory banks. EasyNet [21] inspires this paper and improves 3D industrial anomaly detection performance without utilizing pre-trained models and memory banks. Compared with AST [11], DRAIN utilizes the encoder-...
A memory module is adopted to reduce the reconstruction error, which is capable of enhancing the robustness of the autoencoder as a prototype memory module. The prediction of high-quality future frames can effectively prevent the reconstruction of abnormal frames, and the two branches can be ...
To address this issue, we propose in this work AutoGuard, a proactive anomaly detection solution that employs application-layer Performance Measurement (PM) counters to train two different Deep Learning (DL) techniques, namely, Long Short Term Memory (LSTM) networks and AutoEncoders (AEs). We ...