MoDIR(Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations)使用域对抗训练(DAT)用于稠密检索的无监督领域适应。 UDALM(UDALM: Unsupervised Domain Adaptation through Language Modeling, NAACL 2021)采用多阶段训练,首先在目标域使用MLM预训练,之后采用目标域MLM与源域有监督目标进行多任...
近来的研究主要集中在通过神经网络学习域不变表示(domain invariant representation),以便在标记源数据集上训练的深度学习模型可以迁移到具有少量或无标记样本的目标数据集。上述方法大多是针对图像或文本设计的,由于缺乏旋转不变性,同时在处理图同构上有局限性,故不能再图结构数据上应用。只有少数方法利用了相似图【这种...
中提出，学习domain-specific表示要比domain-invariant表示更容易，因此先学domain-specific表示，再用总的...
Heuristic Domain Adaptation(NIPS 2020)中提出,学习domain-specific表示要比domain-invariant表示更容易,因此先学domain-specific表示,再用总的表示减去domain-specific表示,就可以得到domain-invariant表示。基于这个思路,本文提出了一种启发式的domain adaptation框架。在下面的模型结构图中,F(x)表示图像整体的表示,G(x)...
These are based on extracting domain invariant features using deep adversarial learning. For the unsupervised domain adaptation case, the impact of pseudo-labelling is also investigated. We evaluate on two heterogeneous remote sensing datasets, one being RGB, and the other multi-spectral, for the ...
1.Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned...
Learning Invariant Representations across Domains and Tasks Learning to Match Distributions for Domain ...
: learn domain-invariant representations Introduction 标数据任务重,但是直接从GTA5等游戏场景中合成数据又会有”domain shift”的问题 解决方案:unsupervised domain adaptation, utilize labeled examples from the source domain and aDomain adaptation:连接机器学习(Machine Learning)与迁移学习(Transfer Learning) domain...
之前方法是在feature space进行domain adaption,来发现domain invariant representations, 但是这种方法很难可视化,而且某些时候不能够获取pixel-level和low-level domain shift. 最近的gan在使用cycle一致性约束的GAN在不同的domain上进行图片mapping取得了很好的效果,即使没有使用aligned image pairs....
3.1 Training CNN-based domain invariant representations The target is is to learn a representation that minimizes the distance between the source and target distributions, then we can train a classifier on the source labeled data and directly apply it to the target domain with minimal loss in accu...