Many clustering algorithms have been developed, but most of them perform unsatisfactorily on the data with complex structures. Recently, Adversarial Auto-Encoder (AAE) shows effectiveness on tackling such data by combining Auto-Encoder (AE) and adversarial training, but it cannot effectively extract ...
AutoencoderTrustAdversarial trainingRecommender systems face longstanding challenges in gaining users' trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solutions to those challenges focus on leveraging users' social relationships for inferring the user ...
protein. PMDM could encode the protein semantic context information and spatial context information. The protein point cloud data is fed into an invariant encoder SchNet38to obtain the semantic representationhp. Then the semantic information is fused with the ligand data by the cross-attention layers...
which takes detected keypoints into the clustering algorithm to generate the bird’s components. We then utilize the components into convolutional neural networks (CNNs) to extract the local features for posture recognition. Finally, we fuse the global and local features to execute the bird’s ...
The features learning is carried out by labeled adversarial autoencoders. The classification is performed by a support vector machine to classify a new object as explosive, firearms or non-threatening objects. To show the superiority of our proposed system, a comparative analysis was carried out ...
Typically, they are ineffective for online data stream clustering. Therefore, an efficient online big data stream clustering using dual interactive Wasserstein generative adversarial network (OBDSC-DI-WGAN) is proposed in this paper. The proposed method consists of three phases: data initialization, ...
Specifically, the first insurance is based on generative adversarial network, whose generator is constrained by a clustering method to make the generated samples close to the real samples. The second insurance is based on variational autoencoder, including semantic separation, instance network and ...
Adversarial autoencoders. arXiv 2015, arXiv:1511.05644. [Google Scholar] Yang, M.Y.; Landrieu, L.; Tuia, D.; Toth, C. Muti-modal learning in photogrammetry and remote sensing. ISPRS J. Photogramm. Remote Sens. 2021, 176, 54. [Google Scholar] [CrossRef] Mao, G.; Yuan, Y.; ...
Adversarial autoencoders. arXiv 2015, arXiv:1511.05644. [Google Scholar] Yang, M.Y.; Landrieu, L.; Tuia, D.; Toth, C. Muti-modal learning in photogrammetry and remote sensing. ISPRS J. Photogramm. Remote Sens. 2021, 176, 54. [Google Scholar] [CrossRef] Mao, G.; Yuan, Y.; ...
fusion frameworks can be further categorized into fusion methods based on AEs (auto-encoders) [24], fusion methods using CNN (convolutional neural networks), fusion methods relying on RNNs (recurrent neural networks) [25], and fusion methods utilizing GANs (generative adversarial networks) [26]....