NeurIPS 2019poster67 citations
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
Junran Peng, Ming Sun, ZHAO-XIANG ZHANG, Tieniu Tan, Junjie Yan
Abstract
Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for object detection, mainly because the costly ImageNet pretraining is always required for detectors. Training from scratch, as a substitute, demands more epochs to converge and brings no computation saving.
BibTeX
@inproceedings{NEURIPS2019_3aaa3db6,
author = {Peng, Junran and Sun, Ming and ZHANG, ZHAO-XIANG and Tan, Tieniu and Yan, Junjie},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/3aaa3db6a8983226601cac5dde15a26b-Paper.pdf},
volume = {32},
year = {2019}
}