domainadaptationbybackpropagation[1] 中提出了用对抗的思想进行DomainAdaptation,该方法名叫DANN(或RevGrad)。核心的问题是同时学习分类器G、特征提取器F、以及领域判别器D。通过最小化分类器误差,最大化判别器误差,使得学习到的特征表达具有跨领域不变性。 作者指出,DANN的方法只是关注了数据特征的整体分布,忽略了和...
论文笔记 ——《Unsupervised Domain Adaptation by Backpropagation》,程序员大本营,技术文章内容聚合第一站。
模型的优化和 SGD(随机梯度下降)非常相似,不同在于多了一个 -λ 的系数;这就不能直接作为 SGD 实现了,需要将它更新为 SGD 的形式。这可以用梯度反转层(GRL)实现。 GRL插入在特征提取器和域分类器之间,当反向传播经过GRL时,GRL将反传过来的偏导乘以 -λ ,继续传给上游。 在公式层面,构造一个伪函数,塞进去...
Domain adaptationHeterogeneous dataIncremental learningUnsupervised learningIncremental learning is a learning paradigm in which a model is updated continuously as new data becomes available, and its main challenge is to adapt to non-stationary environments without the time-consuming re-training process. ...
Unsupervised Domain Adaptation by Backpropagation(2015)——DANN学习笔记 tip 本文是第一篇将对抗性训练的思想使用到域适应中的论文。 abstract 现如今,深度体系结构的良好性能大多得益于大量已标注样本下的训练。在某项任务缺乏已标记样本的情况下,如果可以获得特征相似但来自不同领域的已标记样本(例如合成图像),则...
Unsupervised Domain Adaptation by Backpropagation(2015)学习笔记 tip 是第一篇将对抗性训练的思想使用到域适应中的论文。 abstract 现如今,深度体系结构的良好性能大多得益于大量已标注样本下的训练。在某项任务缺乏已标记样本的情况下,如果可
会议:ICML 2015 论文题目:Unsupervised Domain Adaptation by Backpropagation 论文地址:http://proceedings.mlr.press/v37/ganin15.pdf 论文代码:https://github.com/fungtion/DANN 问题描述:深度学习的模型…
2.3.3Unsupervised domain adaptation (UDA) By transferring knowledge from a labeled source domain to an unlabeled target domain, UDA has emerged as a powerful solution formedical imagingscenarios, where acquiring labeled data can be labor-intensive and costly[11]. This adaptability to various medical...
Unsupervised Domain Adaptation by Backpropagation domain classifier that discriminates between the source and the target domains during training. While the parameters of the classifiers are optimized in order to minimize their error on the training set, the parameters of the underlying deep feature...
Domain-invariant kernelText and object recognitionUnsupervised domain adaptation aims to use labeled instances from a source domain to train a good learning model, which can classify unlabeled instances from a target domain as accurate as possible. The biggest challenge is that datasets from the ...