To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined ...
Firstly, we introduce cross-attention as a fine-grained domain adaptation constraint into the CDAG-network, to enhance its capability in analyzing features from real and synthetic domains and aligning their distributions. Secondly, in light of the complex nature of rain artifacts, we propose the Mi...
BMC Bioinformatics (2024) 25:141 https://doi.org/10.1186/s12859-024-05753-2 BMC Bioinformatics RESEARCH Open Access CAT‑DTI: cross‑attention and Transformer network with domain adaptation for drug‑target interaction prediction Xiaoting Zeng1, Weilin Chen2* and Baiying Lei3*...
In unsupervised domain adaptation (UDA), many efforts are taken to pull the source domain and the target domain closer by adversarial training. Most methods focus on aligning distributions or features between the source domain and the target domain. However, little attention is paid to the interact...
本文解读我们 ICLR 2022 上发表的论文《CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation》。这篇文章提出一种基于 Transformer 的跨域方法:CDTrans。它使用 Transformer 中的 CrossAttention 机制来实现 SourceDomain 和 TargetDomain 特征对齐。具体来说,在传统方法给 TargetDomain 打伪标签的过程中...
To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined ...
Domain adaptive person re-identification (re-ID) aims to re-identify persons across domains with distinct distributions. The key to this task lies in how to effectively mitigate the domain gap between source and target domain. We observe that the attention of a network, which is crucial for id...
fDA的思想是合理的,因为多维矩阵可以看作是个多次的转换操作,这里面会有主要,次要和与主方向相抵的转换存在,更多去关注主要方向能起到收敛效果,类似于attention一样。但这里会有一个问题,当训练集与开发集失配程度较大,同时开发集内部很复杂,是从多个不同domain采集到汇集起来的dev set,那直接在这做上采样是不合适...
2.3 CDTrans:Cross-Domain Transformer 框架如下: 上述CDTrans 框架包括三个权重共享的 transformer ,分别是 source branch, source-target branch, target branch 。 输入对中的源图像和目标图像分别被发送到 source branch 和 target branch 。在这两个分支中,self-attention 涉及到学习特定领域的表示。并利用 soft...
To alleviate the problem of sparse target domain data and cold start in cross domain recommendation, Cross-domain recommendation model of deep feature extraction and attention mechanism (CRDFEAM) model is proposed by combining the techniques including th