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 ...
Furthermore, we introduce a dual-channel autoencoder with an attention mechanism to fuse the information from key region images and complete video frames, enhancing the key region features and improving the detection accuracy of the model. In summary, our work makes the following contributions: We...
A memory pool variational autoencoder framework for cross-domain recommendation Cross-domain recommendation (CDR) leverages knowledge from the source domain to make recommendations for the cold-start users in the target domain. On acco... J Yang,J Zhu,PY Zhang - 《Expert Systems with Application》...
Likewise, in their study, Ottom et al. [17] Discuss using the 3D-Znet deep learning network to effectively separate 3D MR brain tumors. The model aims to optimize the representation of features at various hierarchical levels and achieve high performance by utilizing a variable autoencoder Znet ...
Figure 1. VideoMAE with dual masking. To improve the overall efficiency of computation and memory in video masked autoen- coding, we propose to mask the decoder as well and devise the dual masking strategy. Like encoder, we also apply a masking map to the deocod...
Luo et al.18 proposed an improved autoencoder stacking method based on convolutional shortcuts and domain fusion strategies, replacing the sparse term Kullback–Leibler (KL) divergence in the original SAE with convolutional shortcuts, and introducing a domain fusion strategy to share feature ...
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. ...
Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. In NeurIPS, 2022. 2 [66] Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. A closer look at spatiotemporal convolutions for action recognition. In...
Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Khosravi A, Subasi A, Acharya UR, Gorriz JM (2022) Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 73:103417 Article Google Schol...
Domain generalization for object recog- nition with multi-task autoencoders. In Proceedings of the IEEE international conference on computer vision, pages 2551–2559, 2015. 3 [11] Rui Gong, Wen Li, Yuhua Chen, and Luc Van Gool. Dlow: Domain flow...