AutoencoderTrustAdversarial trainingRecommender systems face longstanding challenges in gaining users' trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solut
Hyperspectral image super- resolution by band attention through adversarial learning. IEEE Transactions on Geoscience and Remote Sensing, 58(6):4304–4318, 2020. [28] Ke Li, Dengxin Dai, and Luc Van Gool. Hyperspectral image super-resolution with rgb image super-resoluti...
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...
Similar to VAE-NTMs, these Adversarial-Based Models will also face topic collapse and result in a low level of diversity. To alleviate the topic collapse and the entanglement of topic distribution, Nguyen and Luu (2021) incorporate the contrastive learning into VAE and propose the Contrastive ...
DAEGC [36] DAEGC is a goal-oriented graph attention auto-encoding clustering framework, which is trained in a pre-training-fine- tuning approach. The framework mainly has graph attention auto-encoder and self-training clustering module. MAGCN [37] This algorithm designs two path encoders (one ...
We design the components generation module in the local feature extraction network, 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 ...
Generative adversarial network Image synthesis Deep learning Dual-energy CT Bone marrow edema Data augmentation 1. Introduction Dual-energy computedtomography(DECT) allows the differentiation of materials whose attenuation properties differ at distinct energies (Johnson et al., 2007). One of the most pro...
Dual adversarial deconfounding autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics datadoi:10.1038/s41598-023-45983-7CLINICAL decision support systemsRADIOMICSHODGKIN'S diseasePROGNOSTIC testsIMAGE analysisDEEP learningMedical imaging represents the pr...
generative adversarial networks, liquid state machines, auto encoders, variational auto encoders, denoising auto encoders, sparse auto encoders, extreme learning machines, echo state networks, Markov chains, Hopfield networks, Boltzmann machines, restricted Boltzmann machines, deep residual networks, Kohone...
Specifically, we introduce a double decoder adversarial autoencoder (DDAAE) to align MRIs from different protocols. The aligned MRI images are then integrated into our proposed ensemble residual soft shrinkage threshold attention (ERS 2 TA) diagnostic network for disease diagnosis. This framework not ...