adversarial autoencoder
时间: 2023-04-25 15:01:30 浏览: 69
对抗自编码器(Adversarial Autoencoder)是一种基于生成对抗网络(GAN)的自编码器模型。它通过引入一个判别器网络来强制自编码器生成的样本与真实样本的分布相似,从而提高生成样本的质量。同时,它也可以用于无监督学习和数据压缩等任务。
相关问题
Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思
这段文字讨论了异质网络方法相对于同质网络方法更接近现实的原因,并介绍了知识图谱和知识图谱嵌入模型在链接预测任务中的应用。作者提出了一些方法来解决多信息融合和归纳能力限制等问题,包括使用药物的局部信息如化学结构,以及利用多种信息来提高药物相互作用识别的性能。这段文字主要是关于药物相互作用预测领域的研究。
卷积对抗性自动编码器
卷积对抗性自动编码器(Convolutional Adversarial Autoencoder,CAAE)是基于对抗生成网络(Generative Adversarial Network,GAN)的一种自编码器模型。它通过引入判别器网络来指导编码器-解码器网络的学习,提高了模型的生成能力和图像重建质量。
在CAAE中,编码器和解码器网络都采用卷积神经网络(Convolutional Neural Network,CNN)结构,并使用对抗训练策略来使得生成的图像更加逼真。判别器网络则负责判断输入的图像是真实图像还是编码器生成的假图像,从而指导编码器和解码器网络的学习。整个模型的训练过程是一个迭代的过程,每次迭代中,判别器网络和编码器-解码器网络都会进行参数更新,以提高模型的性能。
CAAE可以被应用于图像生成、图像重建、图像降噪等任务,并在一些视觉任务中取得了较好的效果。