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.

The paper reads very well and manages to present both the challenges of NAS and the proposed idea in a very understandable form (although English grammar and spelling could be improved). The paper's main idea is to constrain the search space of NAS to the dilation factor of convolutions, such that the effective receptive field of units in the network can be varied, while keeping the network weights fixed (or at least allowing the weights to be re-used and smoothly varied during the optimization). This idea is very attractive from a computational point of view, since it allows the notoriously expensive NAS process to achieve faster progress by avoiding the need for ImageNet pre-training after every architecture change. On the flip side, the proposed NATS method only explores part of the potential search space of neural architecture variations. So, the longer-term effect will depend on how restrictive this choice of search space is.

This paper proposes a neural architecture search method specifically for object detection tasks. Although the review scores were initially borderline, the feedback and the subsequent discussion swayed the reviewers into a jointly and consistently positive opinion of the paper. Although the concerns of R5 remain, even this reviewer agrees that they are not sufficient to criticise this work as a whole. I thus recommend to accept this paper.

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.

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.