Efficient neural architecture transformation search in channel-level for object detection

Junran Peng, Ming Sun, Zhaoxiang 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.