论文标题
有关斑块细胞神经过程的研究
Research on Patch Attentive Neural Process
论文作者
论文摘要
细心的神经过程（ANP）提高了神经过程（NP）的拟合能力并提高其预测准确性，但是模型的较高时间复杂性对输入序列的长度施加了限制。受诸如视觉变压器（VIT）和屏蔽自动编码器（MAE）之类的模型的启发，我们使用图像贴片作为输入并改善基于ANP的确定性路径的结构提出了贴片的关注神经过程（PANP），该过程允许模型更准确地提取图像特征，并有效地重建。
Attentive Neural Process (ANP) improves the fitting ability of Neural Process (NP) and improves its prediction accuracy, but the higher time complexity of the model imposes a limitation on the length of the input sequence. Inspired by models such as Vision Transformer (ViT) and Masked Auto-Encoder (MAE), we propose Patch Attentive Neural Process (PANP) using image patches as input and improve the structure of deterministic paths based on ANP, which allows the model to extract image features more accurately and efficiently reconstruction.
