3 research outputs found
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning
Deep convolutional neural networks have been demonstrated to be effective for
SISR in recent years. On the one hand, residual connections and dense
connections have been used widely to ease forward information and backward
gradient flows to boost performance. However, current methods use residual
connections and dense connections separately in most network layers in a
sub-optimal way. On the other hand, although various networks and methods have
been designed to improve computation efficiency, save parameters, or utilize
training data of multiple scale factors for each other to boost performance, it
either do super-resolution in HR space to have a high computation cost or can
not share parameters between models of different scale factors to save
parameters and inference time. To tackle these challenges, we propose an
efficient single image super-resolution network using dual path connections
with multiple scale learning named as EMSRDPN. By introducing dual path
connections inspired by Dual Path Networks into EMSRDPN, it uses residual
connections and dense connections in an integrated way in most network layers.
Dual path connections have the benefits of both reusing common features of
residual connections and exploring new features of dense connections to learn a
good representation for SISR. To utilize the feature correlation of multiple
scale factors, EMSRDPN shares all network units in LR space between different
scale factors to learn shared features and only uses a separate reconstruction
unit for each scale factor, which can utilize training data of multiple scale
factors to help each other to boost performance, meanwhile which can save
parameters and support shared inference for multiple scale factors to improve
efficiency. Experiments show EMSRDPN achieves better performance and comparable
or even better parameter and inference efficiency over SOTA methods.Comment: 21 pages, 9 figures, 5 table
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Differing Pedagogical Methods in the Teaching of Pig Heart Anatomy
Learning modalities play a critical role in shaping educational outcomes, specifically in undergraduate anatomy education. This study examines the effects of two prominent modalities, lecture-based learning and hands-on dissection, in enhancing students' understanding of pig heart anatomy and their self-confidence perceptions. A cohort of 49 undergraduate students enrolled in a human anatomy course participated in the study, which utilized a randomized control design. Participants were divided into two groups: a laboratory dissection learning group and a lecture learning group. Pre and post-tests were administered to assess pig heart anatomy knowledge, and self-confidence questionnaires were administered to assess perceived self-confidence during the intervention. Additionally, retention tests were conducted two weeks post-intervention to evaluate overall retention of the given material. Statistical analyses, including Mann-Whitney U tests, revealed significant differences between the two groups. The lecture group exhibited greater improvements in pig heart anatomy test scores compared to the dissection group in the change in scores from the pre to post test. Despite similarities in baseline knowledge, the lecture group's familiarity with the instructional modality may have contributed to their superior performance. Additionally, significant differences were observed in retention pig heart anatomy test scores as well. There were no significant differences found in the change in self-confidence rates from the pre, post, and retention timeframes of the intervention between groups, but a significant difference was found in the post-test mean scores of the self-confidence questionnaire. This study challenges prior literature that supports the superiority of kinesthetic learning in anatomy education. Further research is needed to evaluate pedagogical practices that provide the most benefits to undergraduate students in anatomy education