Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection

By Junran Peng, Ming Sun, Zhaoxiang Zhang, Tien-Ping Tan, Junjie Yan. 2019

58 citations recorded by the source corpus.

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. To overcome this obstacle, we introduce a practical neural architecture transformation search(NATS) algorithm for object detection in this paper. Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights. We propose a novel neural architecture search strategy in channel-level instead of path-level and devise a search space specially targeting at object detection. With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection. Since our method is gradient-based and only searches for a transformation scheme, the weights of models pretrained in ImageNet could be utilized in both searching and retraining stage, which makes the whole process very efficient. The transformed network requires no extra parameters and FLOPs, and is friendly to hardware optimization, which is practical to use in real-time application. In experiments, we demonstrate the effectiveness of NATS on networks like {\em ResNet} and {\em ResNeXt}. Our transformed networks, combined with various detection frameworks, achieve significant improvements on the COCO dataset while keeping fast.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

A Comprehensive Survey of Neural Architecture Search (2020, methodology): “lation and accelerate the search of the network architecture [18–20, 48, 49, 52, 84, 105]. With the improvement of NAS search efficiency, NAS is also quickly applied in the fields of object detection [65, 75, 111, 118], semantic segmentation [63, 64, 120], adversarial learning [53], architectural sc”
Hierarchical Neural Architecture Search for Deep Stereo Matching (2020, background): “On a separate line of research and to reduce the human efforts in designing neural networks, Neural Architecture Search (NAS) has mounted tremendous successes in various high-level vision tasks such as classification [13, 14, 15], object detection [16, 17], and semantic segmentation [18, 19, 20].”
AdversarialNAS: Adversarial Neural Architecture Search for GANs (2019, methodology): “The NAS technique has applied to many computer vision tasks such as image classiﬁcation [2, 28, 29, 34, 52, 4], dense image prediction [27, 50, 3] and object detection [11, 33].”
Computation Reallocation for Object Detection (2019, background): “Peng et al. (2019) search for dilated rate on channel level in the CNN backbone.”