We propose a novel method for this setting, which first trains a model on each source dataset and then conduct data-free model fusion that fuses the trained models layer-by-layer based on their semantic similarities, which aggregates different levels of semantics from the distributed sources indirectly.
Specifically, it first learns the conditional distribution of input features of one domain given input features of another domain, and then it estimates the domain-invariant relationship by predicting labels with the learned conditional distribution.
In this paper, we investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the Conventional Domain Generalization (CDG).
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted.
A compressive sensing based circular polarization snapshot spectral imaging system is proposed in this paper to acquire two-dimensional spatial, one-dimensional circular polarization (the right and left circular polarization), and one-dimensional spectral information, simultaneously.
Compressive Sensing Optics Image and Video Processing 78A10 (Primary) I.4.2; I.4.4; I.4.5
The accuracy of classification is effectively improved by exploiting the synergy between the deep learning network and coded apertures.
Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning.