Such a process will inevitably introduce mismatched pairs (i. e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain.
To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART).
Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
no code implementations • 11 May 2020 • Junbo Zhao, Marcos Netto, Zhenyu Huang, Samson Shenglong Yu, Antonio Gomez-Exposito, Shaobu Wang, Innocent Kamwa, Shahrokh Akhlaghi, Lamine Mili, Vladimir Terzija, A. P. Sakis Meliopoulos, Bikash Pal, Abhinav Kumar Singh, Ali Abur, Tianshu Bi, Alireza Rouhani
Power system dynamic state estimation (DSE) remains an active research area.
Power system emergency control is generally regarded as the last safety net for grid security and resiliency.