Triplet loss guided adversarial domain adaptation for bearing fault diagnosis
X Wang, F Liu - Sensors, 2020 - mdpi.com
Recently, deep learning methods are becomingincreasingly popular in the field of fault
diagnosis and achieve great success. However, since the rotation speeds and load …
diagnosis and achieve great success. However, since the rotation speeds and load …
Joint contrastive learning for unsupervised domain adaptation
Enhancing feature transferability by matching marginal distributions has led to
improvements in domain adaptation, although this is at the expense of feature …
improvements in domain adaptation, although this is at the expense of feature …
Hard class rectification for domain adaptation
Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich and related
domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has …
domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has …
Domain Agnostic Learning for Unbiased Authentication
Authentication is the task of confirming the matching relationship between a data instance
and a given identity. Typical examples of authentication problems include face recognition …
and a given identity. Typical examples of authentication problems include face recognition …
Unsupervised Domain Adaptation via Joint Contrastive Learning
박창화 - 2021 - s-space.snu.ac.kr
Domain adaptation is introduced to exploit the label information of source domain when
labels are not available for target domain. Previous methods minimized domain discrepancy …
labels are not available for target domain. Previous methods minimized domain discrepancy …
[引用][C] Relaxed Invariant Representation for Unsupervised Domain Adaptation
H Askari Lyarjdameh - 2021