Deep learningRepresentation learningSupervised deep learning requires a huge amount of reference data, which is often difficult and expensive to obtain. Domain adaptation helps with this problem-labelled data from one dataset should help in learning on another unlabelled or scarcely labelled dataset. In...
我们的方法可以应用于任何可以获得一致的性能增益的深度伪造检测模型。 4.1 Learning Domain-invariant Representations ①深度伪造连续学习中,新任务样本的数量通常较少,不能很好地代表新任务数据的分布。因此,我们基于有监督对比学习对齐新旧任务之间的特征,这样有利于新任务的学习和原有知识的保存。 Cross-Entropy Loss. ...
The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard...
This repository contains code for reproducing the experiments reported in the paper Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning published at the International Conference on Learning Representations (ICLR2017) by Werner Zellinger, Edwin Lughofer and Susanne Saminger-Platz fr...
Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis lea...
Moreover, they only rely on the sparse interactions as supervised signals for model training, which can not guarantee the generated representations are effective. In response to the limitation of these existing works, we propose a model named MRCDR which explicitly models relationships between domain...
使用简单反例指出 论文 Analysis of Representations for Domain Adaptation 中上界对保障域泛化的非充分性,四两拨千斤。认为原上限问题出在 λ∗ ,作者认为Since we usually do not have access to the optimal hypothesis on both domains, although the generalization bound still holds on the representation space...
论文翻译:Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation 摘要 监督学习的成功依赖于假设训练和测试数据来自相同的潜在分布,这在实践中往往是无效的,因为潜在的分布转移。鉴于此,现有的非监督域自适应方法大多集中于实现域不变表示和小源域误差。然而,最近的研究表明,这并不足以...
For two input images 𝑋𝑠Xs and 𝑋𝑚1Xm1 with the same size and different style, the output feature representations of the model are denoted as 𝑔(𝑋𝑠)g(Xs) and 𝑔(𝑋𝑚1)g(Xm1). We define the feature invariance loss, also known as style-invariant loss, as follows: ...
Accordingly, the robust appearance flow representations can be used to train a more general 2D key-point appearance flow network 122. Thus, the 2D key-point appearance flow network 122 is incorporated to synthesize an appearance flow for 2D key-points of an image. The use of 2D key-points ...