IEEE Computational Intelligence Magazine - August 2021 - 32
2) Results on COCO
We apply Mask R-CNN [25] with the C4 backbone as the
detector. The rest of the settings are the same as those for the
PASCAL VOC experiment. In Table 4, we can see the results
on the COCO dataset with the C4 backbone. With the 2 #
schedule, FaUNAE is better than its ImageNet supervised
counterpart on all metrics. Due to the absent result of MoCo
v2 [11], we do not compare it with our FaUNAE. We run their
code for this comparison, which is even worse than v1. Also,
FaUNAE is better than ResNet50 trained with unsupervised
MoCo v1 [1].
V. Conclusions
We proposed a fast and unsupervised neural architecture evolution
(FaUNAE) to evolve an existing architecture manually
designed or searched for on one small dataset to a new architecture
on another larger dataset. The evolution is self-supervised,
where contrast loss is used as the evaluation metric in the
student-teacher framework. The evolution process is significantly
accelerated by eliminating the inferior or the least promising
operation. Experimental results demonstrate the
effectiveness of our unsupervised NAE for the downstream
applications, such as object recognition, object detection, and
instance segmentation. FaUNAE addresses core technical challenges
in improving the performance of unsupervised deep
learning to make it more scalable and applicable, which will
ultimately impact many meaningful applications.
Acknowledgments
This study was supported by Grant NO.2019JZZY011101
from the Key Research and Development Program of Shandong
Province to Dianmin Sun. This work was supported in
part by the National Natural Science Foundation of China
under Grant 62076016 and 61876015.
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IEEE Computational Intelligence Magazine - August 2021
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