From: NIPS.cc/2018/Workshop/CDNNRIA/Paper33/Authors
comment: Thank you very much for pointing out the missing key reference. We have added the reference in the revised paper and added a Appendix section which discuss the differences between [1], which is illustrated bellow as well.

The Data Distillation (DD) work of [1] is fundamentally different from our application setting and methods and we show that our domain specific training framework works better for cases on training small models. DD uses a notion that by training with more data on a large capacity model, its performance can be  enhanced. On the other hand, for training DSMs, we use the intention that small capacity models can capture limited domain-specific features well; its performance will be best improved when trained with domain-specific data only. 

We show on Table 3 (Appendix) that following a traditional method of DD (i.e. aggregating PASCAL with domain-specific data) yields lower rmAP improvement than with our approach. This is fundamentally because of the limited model capacity of the compact, but computationally-efficient model. We observe that in cases for training small models, utilizing a larger dataset do not always obtain better results and restricting the data domain can do better.  

[1] Ilija Radosavovic, Piotr Dollar, Ross Girshick, Georgia Gkioxari, and Kaiming He. Data distillation: Towards omni-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4119-4128, 2018.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper33/Authors
comment: 1) 
Thank you for the comment on pointing out that our motivation is unclear. We have modified the introduction in the revised paper, mainly to clearly motivate that we aim to train a "specific" model for each of the stationary surveillance cameras to improve the efficiency of inference. In addition, we emphasized that applications for stationary cameras are emerging, with a expected market growth of 12.3\% to 2026 [1]. Stationary surveillance cameras are expanding its applications to consumer applications as well, e.g. Amazon Ring and Nest. By applying object detection to such cameras, advanced features as detecting unauthorized substances and discovering anomaly events (e.g. a collapsed person) can be realized to further enhance the safety.

[1] Acute Market Reports, (2018, July) Global CCTV Cameras Market Size, Market Share, Application Analysis, Regional Outlook, Growth Trends, Key Players, Competitive Strategies and Forecasts, 2018 To 2026 Retrived from https://www.researchandmarkets.com/research/xv3j26/cctv%E2%80%94cameras?w=4

2) 
Thank you for the comments on the novelty of the work. We have modified the paper to clarify our main contributions.

To the best of our knowledge, our work shows evidence that knowledge distillation can compress a general-purpose object detection model (ResNet-101 Faster-RCNN) trained on COCO to a ResNet-18 Faster-RCNN model trained on unlabelled, domain-specific data with a high accuracy. Moreover, we added a appendix section to clarify that domain specific training is fundamentally different from techniques as Data Distillation, and showed that training with domain-specific data can hold a higher accuracy for small models. Second, we introduce the idea of culling easy-to-classify images to significantly improve the training efficiency with little penalty to accuracy. This feature is important, because such (re-)training may be required to be executed on the edge.

3)
Thank you for pointing out that comparison results are lacking.
MobileNet-1.0 and MobileNet-0.5 are similar to ResNet-18 and SqueezeNet respectively (similar accuracy for ImageNet and number of parameters) and ShuffleNet also holds a similar performance range but with improved computation efficiency. Therefore these results should be similar to that of ResNet-18 and SqueezeNet when used as a backbone for Faster RCNN. We added this discussion in the revised paper.

While we chose the Faster-RCNN architecture which achieved the highest detection accuracy on YoutubeLive datasets, yolo/ssd architecture will provide more computation efficiency.
However, we can extend the proposed framework to yolo/ssd as well, because the training only requires bounding box labels and not region proposal network outputs. Such discussions were added to the revised paper as well. While this is a on-going research, we expect that a domain-specific Resnet-18 backbone YOLO can be trained with a teacher of Faster-RCNN Resnet-101 to achieve a accurate but further computationally efficient model.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper33/Authors
comment: 1) 
Thank you for your comments.
We agree that the current measurement results cannot decouple either the performance improvement comes from the domain specific nature or from the additional data from YouTubeLive dataset.
Therefore, to clarify the contributions, we performed additional experiments in the revised paper (please see Appendix). 
We prepared a dataset similar to the Data Distillation [1] (i.e. aggregating PASCAL with YoutubeLive data) and also a dataset with the entire YoutubeLive data. According to the results (Table 3), we observed that Data Distillation and YoutubeLive dataset yields a smaller improvement than our approach. This is fundamentally because of the limited network capacity of the compact models.

Thank you for the concerns about the ablation study of the designed metric Ltrain. 
In table 2, we compare the DSM training accuracy with two dataset construction strategy. DDS, which picks N data based on the metric derived by Ltrain and Simple, which picks the first N data sequentially (without considering Ltrain). 

2)
Thank you for pointing out about the comparison with other literature.
[2] successfully applies knowledge distillation to Faster-RCNN based object detection models, but their goals are to improve the accuracy for large scale dataset tasks (e.g. COCO). For our best knowledge, we are the first to demonstrate training domain specific models so direct comparison is difficult.

According to [2], their "Tucker model" has 11M parameters with a Faster-RCNN architecture and performs COCOmAP@[.5,.95]=11.8\% which is similar to our Squeeze model (6M para. with  COCOmAP@[.5,.95]=11.7\%). With their Knowledge Distillation (KD) and hint learning framework, they improve the accuracy by 0.8\% with a VGG16 teacher, which is quite limited. Therefore, we estimate the accuracy improvement for the Squeeze model will be similar to that, trained with the same KD framework in [2]. We estimate that the improvement observable for the YoutubeLive will be limited as well. 

By conducting domain specific training, the Squeeze domain specific model can achieve a rmAP of 63.3\% on the YoutubeLive dataset, which is actually higher than the COCO trained off-the-shelf Res18 models (rmAP=54.5\%). It is also worth noting that the Res18 model achieves a significantly higher accuracy than Squeeze for COCO (COCOmAP@[.5,.95]=19.6\%).

3)
Thank you for pointing out the typos. The typos has been fixed in the revised paper.

[1] Ilija Radosavovic, Piotr Dollar, Ross Girshick, Georgia Gkioxari, and Kaiming He. Data distillation: Towards omni-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4119-4128, 2018.
[2] Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker. Learning efficient object detection models with knowledge distillation. In Advances in Neural Information Processing Systems,pages 742–751, 2017.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper33/AnonReviewer3
title: Seems the idea has already been published.
review: Summary:
This paper proposes a framework for training domain specific models for object detection tasks. 
The authors adopt the predictions of a teacher model pretrained on large-scale datasets as the labels of target domain, and then train the student network in the domain-specific data.
Furthermore, in order to reduce the training time of student network, the authors compare the predictions between student and teacher to get hard samples. The results show the training on the difficult dataset (DDS) can yield promising performance with few samples. 

Weaknesses:
An important reference [1] is missing. The novelty is limited since the strategy of DSMs seems the same as the method proposed by [1].  The authors should add more discussion about the differences between [1] and this paper.

[1] Radosavovic, Ilija and Doll{\'a}r, Piotr and Girshick, Ross and Gkioxari, Georgia and He, Kaiming. "Data Distillation: Towards omni-supervised learning." CVPR 2018.
rating: 2: Marginally below acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper33/AnonReviewer2
title: Novelty is limited. Experiments are not sufficient.
review: This paper tries to improve efficiency of object detection model by limiting the application to a specific domain, then applying knowledge-distillation and dataset pruning to obtain a smaller-size model. Experimental results seem ok. 

I have following concerns on the paper: 

- the idea of limiting to specific domain is interesting but the current scope is too limited to match realistic applications. We may want to detect certain fine-grained object classes such as pedestrians, cars; but it is unusual that we only want to detect certain object at a specific view. The authors need to make the motivation clearer and more convincing. 

- the methods are standard and presented in many existing works. Using knowledge distillation is a common practice currently for slightly compressing the model. The authors need to make the novelty clearer. 

- comparison with small-size object detection model is lacking. need to compare with Mobile-net, shuffle-net, yolo/ssd.
rating: 2: Marginally below acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper33/AnonReviewer1
title: An easy framework to train a domain-specific detection model
review: This paper propose a method to train a domain specific detection model using a teacher-student network based on the intuition that misdetections usually occur between foreground and background but rarely are misclassified. 

The method is quite effective and the algorithm is easy to follow. But I have some concerns about the experimental section:

1)	Table 1 compares the performance (relative mAP) on YouTubeLive of DSM with Faster-RCNN pre-trained on COCO. Because the DSM has an additional training on YouTubeLive dataset, the performance improvement does not demonstrate the value of your proposed algorithm. I would suggest a comparison between DSM and the student model trained on YouTubeLive dataset, or some other methods without teacher-student models. I also expect an ablation study to test the effectiveness of your designed metric L_train, which enables the domain-specific transfer process.
2)	I do not see any comparison between your methods and other literature, such as the “traditional techniques” mentioned in your abstract but not appeared in paper.
3)	Typo? Line 82 should be “3.6% accuracy penalty”

rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct