Transfer LearningNeural network-based models have recently shown excellent performance in various kinds of tasks. However, a large amount of labeled data is required to train deep networks, and the cost of gathering labeled training data for every kind of domain is prohibitively expensive. Domain ...
In this study, domain‐invariant adversarial learning with conditional distribution alignment is proposed to alleviate the effect of domain shift with label shift. To obtain the domain‐invariant features, the proposed method modifies adversarial auto‐encoder architecture and performs semi...
Domain Invariant Representation Learning with Domain Density Transformations. NeurIPS'21 作者:A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin [Transfer Learning] 这篇文章关注 Domain generalization问题,DG希望借助来自若干domain的数据建立模型,期望模型在未见domain上有较小的风险。
In this paper, we thus propose a simple but effective model for unsupervised domain adaption leveraging adversarial learning. The same encoder is shared between the source and target domains which is expected to extract domain-invariant representations with the help of an adversarial discriminator. ...
LEARNING FORDOMAIN-INVARIANTTRAINING(针对领域不变训练的注意力对抗学习) 简介 基于注意力机制的领域不变对抗性训练,用于抑制说话人变量与环境变量,以实现鲁棒... 深度特征序列中每个特征的分类错误之和。然而,与无话语帧的深度特征相比,有话语帧的深度特征更具有领域鉴别性;与辅音相比,元音的深度特征的领域变化性更...
[Transfer Learning] 这篇文章讨论了Unsupervised Domain Adaptation中如何构建不变表示。 UDA中假设存在充分的来自源分布DS的标记数据和来自目标分布DT的无标记样本。由于来自目标分布的样本没有标记,UDA中的一些工作希望能将来自两个分布的样本映射到同一个子空间:Xs→Z,Xt→Z,然后基于源分布的标记数据来训练分类器Z...
(CoRL2020)DIRL: Domain-Invariant Representation Learning Approach for Sim-to-Real Transfer 论文笔记,程序员大本营,技术文章内容聚合第一站。
We also propose a new adaptive triplet loss to boost the metric learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted...
Domain generalization semantic segmentation methods aim to generalize well on out-of-distribution scenes, which is crucial for real-world applications. Recent works focus on learning domain-invariant content information by using normalization, whitening, and domain randomization to remove style information....
Remi Tachet des Combes, Kun Zhang, Geoff Gordon International Conference on Machine Learning|June 2019 Download BibTex 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 achie...