Original: F AST N EURAL N ETWORK A DAPTATION VIA P ARAMETERS R EMAPPING Anonymous authors
Revised: F AST N EURAL N ETWORK A DAPTATION VIA P ARAMETER R EMAPPING AND A RCHITECTURE S EARCH

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Deep neural networks achieve remarkable performance in many computer vision tasks.
Revised: Deep neural networks achieve remarkable performance in many computer vision tasks.

Original: For most networks in semantic segmentation(seg) and object detection (det) tasks, the backbone directly reuses the network manually designed for  classiﬁcationtasks. Utilizing a network pre-trained on ImageNet as the backbone has been a popular practice for seg/det tasks.
Revised: Most state-of-the-art ( SOTA ) semantic segmentation and object detection approaches reuse neural network architectures designed for image classiﬁcation as the backbone, commonly pre-trained on ImageNet.

Original: However,because of the gaps between different tasks, adapting the network to the target task brings performance promotion. Some recent neural architecture search (NAS) methods search for the backbone of seg/det networks.
Revised: However, performance gains can be achieved by designing network architectures speciﬁcally for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation.

Original:  ImageNet pre-training of the search space representation  or the searched network bears huge computational cost.
Revised: One major challenge though, is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost.

Original: In this paper, we propose a fast neural network adaptation (FNA) method, which can adapt both the architecture and parameters of amanually designed seed network to the new seg/det tasks efﬁciently. A parameter remapping mechanism is designed  to accelerate the whole adaptation process which takes full advantage of the knowledge from the seed network. The NASmethod is utilized for architecture adaptation.
Revised: In this paper, we propose a Fast Neural Network Adaptation (FNA) method, which can adapt both the architecture and parameters of a seed network (e.g. a high performing manually designed backbone) to become a network with different depth, width, or kernels via a Parameter Remapping technique, making it possible to utilize NAS for detection/segmentation tasks a lot more efﬁciently.

Original: In  experiments, we conduct network adaptation onthe MobileNetV2network to  both seg and det tasks. FNA demonstrates clear performance gains compared with both manually and NAS designed networks.
Revised: In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.

Original: The total computational cost of FNA is signiﬁcantly less thanmany SOTA seg/det NAS methods: 1737 × less than DPC, 6.8 × less than Auto-DeepLab and 7.4 × less than DetNAS. 
Revised: The total computation cost of FNA is signiﬁcantly less than SOTA segmentation/detection NAS approaches: 1737 × less than DPC, 6.8 × less than Auto-DeepLab and 7.4 × less than DetNAS. The code is available at https://github.com/JaminFong/FNA .

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Deep  neural networks(DNNs) have achieved great successes inrecent years. Especially, they are widely applied in computer vision tasks, e.g., image classiﬁcation (Krizhevsky et al., 2012;
Revised: Deep convolutional neural networks have achieved great successes in computer vision tasks such as image classiﬁcation (Krizhevsky et al., 2012;

Original: He et al., 2016; Howard et al., 2017), semantic segmentation (Long et al., 2015; Ronneberger et al., 2015; Chen et al., 2017b) and object detection (Ren et al., 2015; Liu et al., 2016; Lin et al., 2017) etc.
Revised: He et al., 2016; Howard et al., 2017), semantic segmentation (Long et al., 2015; Ronneberger et al., 2015; Chen et al., 2017b) and object detection (Ren et al., 2015; Liu et al., 2016; Lin et al., 2017) etc.

Original: Image classiﬁcation  always serves as a fundamental task for neural network designing.
Revised: Image classiﬁcation has always served as a fundamental task for neural architecture design.

Original: In general, one network elaborately designed and pre-trained on the classiﬁcation taskis directly used as the backbone of the segmentation or detection network.
Revised: It is common to use networks designed and pre-trained on the classiﬁcation task as the backbone and ﬁne-tune them for segmentation or detection tasks.

Original: The ﬁne-tuning technique is usually utilized to transfer the backbone network to the new task.

Original: There is no doubt that the backbone plays an important role in segmentation or detection tasks. However, there are gaps between various tasks which lead to the  different  principles of architecture designing.
Revised: However, the backbone plays an important role in the performance on these tasks and the difference between these tasks calls for different design principles of the backbones.

Original: For example, segmentation tasks require high-resolution features and object detection tasks need to infer both localization and classiﬁcation information from each convolutional feature.
Revised: For example, segmentation tasks require high-resolution features and object detection tasks need to make both localization and classiﬁcation predictions from each convolutional feature.

Original: Therefore, neural architectures designed for classiﬁcation tasks do not sufﬁce well for segmentation or detection tasks.
Revised: Such distinctions make neural architectures designed for classiﬁcation tasks fall short.

Original: To tackle this problem, some works (Li et al., 2018; Wang et al., 2019) propose to improve the backbone architecture designing for better performance on the target tasks.
Revised: Some attempts (Li et al., 2018; Wang et al., 2019) have been made to tackle this problem.

Original: Handcrafted neural architecture design is inefﬁcient and may not ﬁnd the best-performing networks.
Revised: Handcrafted neural architecture design is inefﬁcient, requires a lot of human expertise, and may not ﬁnd the best-performing networks.

Original: Recently, neural architecture search (NAS) methods (Zoph et al., 2017; Pham et al., 2018; Liu et al., 2018; 2019b; Cai et al., 2019) aim at designing the architectures of DNNs automatically, which can alleviate the burden of human experts.
Revised: Recently, neural architecture search (NAS) methods (Zoph et al., 2017; Pham et al., 2018; Liu et al., 2018) see a rise in popularity. Some works (Liu et al., [Figure]

Original: Some works (Liu et al., 2019a; Zhang et al., 2019; Chen et al., 2019b) propose to use NASmethods to specialize architecture designing forthe backbones of segmentation or detection networks.
Revised: 2019a ; Zhang et al., 2019; Chen et al., 2019b) propose to use NAS to design backbone architectures speciﬁcally for segmentation or detection tasks.

Original: Nevertheless, pre-training has always been a non-negligible but costly procedure.
Revised: Nevertheless, pre-training remains a inevitable but costly procedure.

Original: Though it is demonstrated that pre-training is not so necessary recently (He et al., 2018), training from scratch on the target task still takes more iterations than ﬁne-tuning from the pre-trained model.
Revised: Though some (He et al., 2018) recently demonstrates that pre-training is not always necessary for accuracy considerations, training from scratch on the target task still takes more iterations than ﬁne-tuning from a pre-trained model.

Original: In architecture search methods, the pre-training cost is non-negligible for[Figure] evaluating the networks in the search space.
Revised: For NAS methods, the pre-training cost is non-negligible for evaluating the networks in the search space.

Original: One-shot search methods (Brock et al., 2017; Bender et al., 2018;
Revised: One-shot search methods (Brock et al., 2017; Bender et al., 2018;

Original: Chen et al., 2019b) integrate all sub-architectures in one super network while pretraining the super network and the searched network still bears large computational cost.
Revised: Chen et al., 2019b) integrate all possible architectures in one super network but pre-training the super network and the searched network still bears huge computation cost.

Original: As ImageNet (Deng et al., 2009) pre-training has been an incredibly common manner in many computer vision tasks, there are lots of models trained on ImageNet available in the community.
Revised: As ImageNet (Deng et al., 2009) pre-training has been a standard practice for many computer vision tasks, there are lots of models trained on ImageNet available in the community.

Original: To take full advantages of these models, we propose a fast neural network adaptation (FNA) method based on a novel parameter-remapping paradigm.
Revised: To take full advantages of these models, we propose a Fast Neural Network Adaptation (FNA) method based on a novel parameter remapping paradigm.

Original: Our method can adapt both the architecture and parameters of one network to a new task with very low computational cost.
Revised: Our method can adapt both the architecture and parameters of one network to a new task with negligible cost.

Original: Fig.
Revised: Fig.

Original: 1 shows the whole framework.
Revised: 1 shows the whole framework.

Original: The adaptation acts on both the architecture- and parameter-level.
Revised: The adaptation is performed on both the architecture- and parameter-level.

Original: We adopt the NAS methods (Zoph et al., 2017; Real et al., 2018;
Revised: We adopt the NAS methods (Zoph et al., 2017; Real et al., 2018;

Original: Liu et al., 2019b) to implement the architecture-level adaptation.
Revised: Liu et al., 2019b) to implement the architecture-level adaptation.

Original: We select a manually designed network (MobileNetV2 (Sandler et al., 2018) in our experiments) as the seed network , which is pre-trained on ImageNet.
Revised: We select a manually designed network (MobileNetV2 (Sandler et al., 2018) in our experiments) as the seed network , which is pre-trained on ImageNet.

Original: Then, we expand the seed network to a super network which is the representation of the search space in FNA.
Revised: Then, we expand the seed network to a super network which is the representation of the search space in FNA.

Original: New parameters in the super network are initialized as one mapping of that in the seed network.
Revised: New parameters in the super network are initialized by mapping those from the seed network using parameter remapping.

Original: Beneﬁting from the proposed parameter-remapping paradigm, the neural architecture searchprocess can be performed efﬁciently on the target task.
Revised: Thanks to that, the neural architecture search can be performed efﬁciently on the detection and segmentation tasks.

Original: With the NAS based architecture adaptation, we obtain a new  architecture targeted at the new task.
Revised: With FNA we obtain a new optimal target architecture for the new task.

Original: Similarly, we remap the parameters of the seed network to the targetarchitecture. Then the target architecture is well-initialized and can be directly ﬁne-tuned on the target task with no need of pre-training on a large-scale dataset.
Revised: Similarly, we remap the parameters of the seed network to the target architecture for initialization and ﬁne-tune it on the target task with no need of pre-training on a large-scale dataset.

Original: Our proposed FNA method aims at adapting both the architecture and parameters of neural network to new tasks efﬁciently.

Original: Based on the parameter-remapping mechanism, FNA takes full advantages of the pre-trained parameters of the seed network.

Original: The remapping mechanism greatly degrades the computational cost of network adaptation.

Original: The effectiveness and efﬁciency of our method are demonstrated by experiments on both segmentation and detection tasks.
Revised: We demonstrate FNA’s effectiveness and efﬁciency via experiments on both segmentation and detection tasks.

Original: We adapt the manually designed network MobileNetV2 (Sandler et al., 2018) to segmentation framework DeepLabv3 (Chen et al., 2017b), detection framework RetinaNet (Lin et al., 2017) and SSDLite (Liu et al., 2016; Sandler et al., 2018).
Revised: We adapt the manually designed network MobileNetV2 (Sandler et al., 2018) to segmentation framework DeepLabv3 (Chen et al., 2017b), detection framework RetinaNet (Lin et al., 2017) and SSDLite (Liu et al., 2016; Sandler et al., 2018).

Original: Networks adapted by FNA surpass both manually designed and NAS searched networks in terms of both performance and model MAdds.
Revised: Networks adapted by FNA surpass both manually designed and NAS searched networks in terms of both performance and model MAdds.

Original: Compared to NAS methods, FNA takes much less computational cost, 1737 × less than DPC (Chen et al., 2018a), 6.8 × less than AutoDeepLab (Liu et al., 2019a) and 7.4 × less than DetNAS (Chen et al., 2019b).
Revised: Compared to NAS methods, FNA costs 1737 × less than DPC (Chen et al., 2018a), 6.8 × less than Auto-DeepLab (Liu et al., 2019a) and 7.4 × less than DetNAS (Chen et al., 2019b).

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Neural Architecture Search With reinforcement learning (RL) and evolutionary algorithm (EA) being applied to NAS methods, many works (Zoph & Le, 2016; Zoph et al., 2017; Real et al., 2018) make great progress in promoting the performances of neural networks.
Revised: Neural Architecture Search With reinforcement learning (RL) and evolutionary algorithm (EA) being applied to NAS methods, many works (Zoph & Le, 2016; Zoph et al., 2017; Real et al., 2018) make great progress in promoting the performances of neural networks.

Original: Recently, NAS methods based on one-shot model (Brock et al., 2017; Bender et al., 2018) or differentiable representa- [Figure] et al.
Revised: Recently, NAS methods based on one-shot model (Brock et al., 2017; Bender et al., 2018) or differentiable representations (Liu et al., 2019b; Cai et al., 2019; Fang et al., 2019b) achieve remarkable results with low search cost.

Original: 2019b Cai et al., 2019; Wu et al., cost use the differentiable NAS method to implement architecture adaptation, which adjusts the architecture of the backbone network automatically.
Revised: We use the differentiable NAS method to implement architecture adaptation, which adjusts the architecture of the backbone network automatically.

Original: Backbone Design As deep neural network designing (Simonyan & Zisserman, 2014; Szegedy et al., 2016;
Revised: Backbone Design As deep neural network designing (Simonyan & Zisserman, 2014; Szegedy et al., 2016;

Original: He et al., 2016) develops, the backbones of segmentation or detection networks evolve accordingly.
Revised: He et al., 2016) develops, the backbones of segmentation or detection networks evolve accordingly.

Original: Some works improve the backbone architectures by modifying existing networks.
Revised: Some works improve the backbone architectures by modifying existing networks.

Original: PeleeNet (Wang et al., 2018) proposes a variant of DenseNet (Huang et al., 2017) for more realtime object detection on mobile devices.
Revised: PeleeNet (Wang et al., 2018) proposes a variant of DenseNet (Huang et al., 2017) for more realtime object detection on mobile devices.

Original: DetNet (Li et al., 2018) applies dilated convolution (Yu & Koltun, 2016) in the backbone to enlarge the receptive ﬁeld which helps to detect objects.
Revised: DetNet (Li et al., 2018) applies dilated convolution (Yu & Koltun, 2016) in the backbone to enlarge the receptive ﬁeld which helps to detect objects.

Original: BiSeNet (Yu et al., 2018) and HRNet (Wang et al., 2019) design multiple paths to learn both highand
Revised: BiSeNet (Yu et al., 2018) and HRNet (Wang et al., 2019) design multiple paths to learn both highand

Original: low- resolution representations for better dense prediction.
Revised: low- resolution representations for better dense prediction.

Original: Parameters Remapping Net2Net (Chen et al., 2015) proposes the function-preserving transformations to remap the parameters of one network to a new deeper or wider network.
Revised: Parameter Remapping Net2Net (Chen et al., 2015) proposes the function-preserving transformations to remap the parameters of one network to a new deeper or wider network.

Original: This remapping mechanism accelerates the training of the new larger network and achieves great performances.
Revised: This remapping mechanism accelerates the training of the new larger network and achieves great performances.

Original: Following this manner, EAS (Cai et al., 2018) extends the parameter-remapping concept to neural architecture search.
Revised: Following this manner, EAS (Cai et al., 2018) extends the parameter remapping concept to neural architecture search.

Original: Moreover, some NAS works (Pham et al., 2018; Fang et al., 2019; Elsken et al., 2019) apply parameters sharing on child models to accelerate the search process.
Revised: Moreover, some NAS works (Pham et al., 2018; Fang et al., 2019a; Elsken et al., 2019) apply parameters sharing on child models to accelerate the search process.

Revised: Our parameter remapping paradigm extends the mapping dimension with the kernel level.

Revised: Parameters can be also mapped to a shallower or narrower network with our scheme, while Net2Net focuses on mapping parameters to a deeper and wider network.

Original: Our parameterremapping paradigm makes the network adaptation get rid of thecost of ImageNet pre-training and largely decreases the computational cost.
Revised: The parameter remapping in our FNA largely decreases the computation cost of the network adaptation by taking full advantages of the ImageNet pre-trained parameters.

Original:  M ETHOD
Revised:  M ETHOD

Original: As the most commonly used network for designing search space in NAS methods (Tan et al., 2018;
Revised: As the most commonly used network for designing search spaces in NAS methods (Tan et al., 2018;

Original: Cai et al., 2019; Wu et al., 2018), MobileNetV2 (Sandler et al., 2018) is selected as the seed network to give the details of our method.
Revised: Cai et al., 2019; Fang et al., 2019b), MobileNetV2 (Sandler et al., 2018) is selected as the seed network to give the details of our method.

Original: To adapt the network to segmentation and detection tasks, we adjust the architecture elements on three levels, i.e., convolution kernel size, depth and width of the network.
Revised: To adapt the network to segmentation and detection tasks, we adjust the architecture elements on three levels, i.e., convolution kernel size, depth and width of the network.

Original: In this section, we ﬁrst describe the parameter-remapping paradigm.
Revised: In this section, we ﬁrst describe the parameter remapping paradigm.

Original: Then we explain the whole procedure of the network adaptation.
Revised: Then we explain the whole procedure of the network adaptation.

Original: 3. P ARAMETERS R EMAPPING
Revised: 3. P ARAMETER R EMAPPING

Original: We deﬁne parameter-remapping as one paradigm which aims at mapping the parameters of one seed network to another one.
Revised: We deﬁne parameter remapping as one paradigm which aims at mapping the parameters of one seed network to another one.

Original: We denote the seed network as N s and the new network as N n , whose parameters are denoted as W s and W n respectively.
Revised: We denote the seed network as N s and the new network as N n , whose parameters are denoted as W s and W n respectively.

Original: The remapping paradigm is illustrated in the following three aspects.
Revised: The remapping paradigm is illustrated in the following three aspects.

Original: The remapping on the depth-level is ﬁrstly carried out and then the remapping on the kernel- and width- level is conducted simultaneously.
Revised: The remapping on the depth-level is ﬁrstly carried out and then the remapping on the kernel- and width- level is conducted simultaneously.

Original: Moreover, we study different remapping strategies in the experiments (Sec. 4.5).
Revised: Moreover, we study different remapping strategies in the experiments (Sec. 4.5).

Original: Remapping on Depth-level We introduce more depth settings in our architecture adaptation process.
Revised: Remapping on Depth-level We introduce more depth settings in our architecture adaptation process.

Original: In other word, we adjust the number of inverted residual blocks (MBConvs)
Revised: In other word, we adjust the number of inverted residual blocks (MBConvs)

Original: (Sandler et al., 2018) in every stage of the network.
Revised: (Sandler et al., 2018) in every stage of the network.

Original: We assume that one stage in the seed network N s has l layers.
Revised: We assume that one stage in the seed network N s has l layers.

Original: Its parameters of each layer can be denoted as { W (1) s , W (2) s , . . .
Revised: The parameters of each layer can be denoted as { W (1) s , W (2) s , . . .

Original: , W ( l ) s } .
Revised: , W ( l ) s } .

Original: Similarly, we assume that the corresponding stage with m layers in the new network N n has parameters { W (1) n , W (2) n , . . .
Revised: Similarly, we assume that the corresponding stage with m layers in the new network N n has parameters { W (1) n , W (2) n , . . .

Original: , W ( m ) n } .
Revised: , W ( m ) n } .

Original: The remapping process on the depth-level is shown in Fig. 2(a).
Revised: The remapping process on the depth-level is shown in Fig. 2(a).

Original: The parameters of layers in N n which also exit in N s are just copied from N s .
Revised: The parameters of layers in N n which also exit in N s are just copied from N s .

Original: The parameters of new layers are all copied from the last layer in the stage of N s .
Revised: The parameters of new layers are all copied from the last layer in the stage of N s .

Original: It is formulated as
Revised: It is formulated as

Original: [Equation]
Revised: [Equation]

Original: Remapping on Width-level In the MBConv block of MobileNetV2 (Sandler et al., 2018) network, the ﬁrst point-wise convolution expands the low-dimensional features to a high dimension.
Revised: Remapping on Width-level In the MBConv block of MobileNetV2 (Sandler et al., 2018) network, the ﬁrst point-wise convolution expands the low-dimensional features to a high dimension.

Revised: [Figure]

Original: This practice can be utilized for expanding the width and capacity of one neural network.
Revised: This practice can be utilized for expanding the width and capacity of one neural network.

Original: We allow smaller expansion ratios for architecture adaptation.
Revised: We allow smaller expansion ratios for architecture adaptation.

Original: We denote the parameters of the convolution in N s as W s ∈ R p × q × h × w and that in N n as W n ∈ R r × s × h × w , where r ≤ p and s ≤ q .
Revised: We denote the parameters of one convolution in N s as W s ∈ R p × q × h × w and that in N n as W n ∈ R r × s × h × w , where r ≤ p and s ≤ q .

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 2(b), on the width-level, we directly map the ﬁrst r or s channels of parameters in N s to the narrower one in N n .
Revised: 2(b), on the width-level, we directly map the ﬁrst r or s channels of parameters in N s to the narrower one in N n .

Original: It can be formulated as
Revised: It can be formulated as

Original: [Equation]
Revised: [Equation]

Original: Remapping on Kernel-level The kernel size is commonly set as 3 × 3 in most artiﬁcially-designed networks.
Revised: Remapping on Kernel-level The kernel size is commonly set as 3 × 3 in most artiﬁcially-designed networks.

Original: To expand the receptive ﬁeld and capture abundant features in segmentation or detection tasks, we introduce larger kernel size settings in the adaptation process.
Revised: To expand the receptive ﬁeld and capture abundant features in segmentation or detection tasks, we introduce larger kernel size settings in the adaptation process.

Original: As Fig. 2(c) shows, to expand the 3 × 3 kernel to a larger one, we assign the parameters of the central 3 × 3 region in the large kernel with the values of the original 3 × 3 kernel.
Revised: As Fig. 2(c) shows, to expand the 3 × 3 kernel to a larger one, we assign the parameters of the central 3 × 3 region in the large kernel with the values of the original 3 × 3 kernel.

Original: The values of the other region surrounding the central part are assigned with 0.
Revised: The values of the other region surrounding the central part are assigned with 0.

Original: We denote the parameters of the original 3 × 3 kernel as W 3 ×and the larger k × k kernel as W k × k .
Revised: We denote the parameters of the original 3 × 3 kernel as W 3 ×and the larger k × k kernel as W k × k .

Original: The remapping process on kernel-level can be formulated as follows,
Revised: The remapping process on kernel-level can be formulated as follows,

Original: [Equation]
Revised: [Equation]

Original: where h, w denote the indices of the spatial dimension.
Revised: where h, w denote the indices of the spatial dimension.

Original: This design principle conforms to the function-preserving concept (Chen et al., 2015), which accelerates and stabilizes the optimization of the new network.
Revised: This design principle conforms to the function-preserving concept (Chen et al., 2015), which accelerates and stabilizes the optimization of the new network.

Original: 3. N EURAL N ETWORK A DAPTATION
Revised: 3. N EURAL N ETWORK A DAPTATION

Original: We divide our neural network adaptation into three steps.
Revised: We divide our neural network adaptation into three steps.

Original: Fig.
Revised: Fig.

Original: 1 demonstrates the whole adaptation procedure.
Revised: 1 demonstrates the whole adaptation procedure.

Original: Firstly, we expand the seed network to a super network which is the representation of the search space in the latter architecture adaptation process.
Revised: Firstly, we expand the seed network to a super network which is the representation of the search space in the latter architecture adaptation process.

Original: Secondly, we utilize the differentiable NAS method to implement network adaptation on the architecture-level and obtain the target architecture Arch t .
Revised: Secondly, we perform the differentiable NAS method to implement network adaptation on the architecture-level and obtain the target architecture Arch t .

Original: Finally, we adapt the parameters of the target architecture and obtain the target network N t .
Revised: Finally, we adapt the parameters of the target architecture and obtain the target network N t .

Original: The aforementioned parameter-remapping mechanism is deployed before the two stages, i.e. architecture adaptation and parameter adaptation.
Revised: The aforementioned parameter remapping mechanism is deployed before the two stages, i.e. architecture adaptation and parameter adaptation.

Original: Network Expansion We expand the seed network to a super network by introducing more options for architecture elements.
Revised: Network Expansion We expand the seed network to a super network by introducing more options for architecture elements.

Original: For every MBConv layer, we allow for more kernel size settings { 3 , 5 , 7 } and more expansion ratios { 3 , 6 } .
Revised: For every MBConv layer, we allow for more kernel size settings { 3 , 5 , 7 } and more expansion ratios { 3 , 6 } .

Original: As most differentiable NAS methods (Liu et al., 2019b; Cai et al., 2019; Wu et al., 2018) do, we relax every layer as a weighted sum of all candidate operations.
Revised: As most differentiable NAS methods (Liu et al., 2019b; Cai et al., 2019; Wu et al., 2018) do, we relax every layer as a weighted sum of all candidate operations.

Original: [Equation]
Revised: [Equation]

Revised: [Table]

Original: (4) where O denotes the operation set, α io denotes the architecture parameter of operation o in the i th layer, and x denotes the input tensor.
Revised: [Table] where O denotes the operation set, α io denotes the architecture parameter of operation o in the i th layer [Table] and x denotes the input tensor.

Original: We set more layers in one stage of the super network and add the identity connection to the candidate operation set for depth search.
Revised: We set more layers in one stage of the super network and add the identity connection to the candidate operation set for depth search.

Original: After expanding the seed network to a super network, we remap the parameters of the seed network to the super network based on the paradigm illustrated in Sec. 3.1.
Revised: After expanding the seed network to a super network, we remap the parameters of the seed network to the super network based on the paradigm illustrated in Sec. 3.1.

Original: This remapping strategy prevents the huge cost of
Revised: This remapping strategy prevents the huge cost of

Original: ImageNet pre-training involved in the search space, i.e. the super network in differentiable NAS.[Table]
Revised: ImageNet pre-training involved in the search space, i.e. the super network in differentiable NAS.

Original: [Table] Architecture Adaptation We start the differentiable NAS process with the expanded super network directly on the target task, e.g., semantic segmentation or object detection.
Revised: Architecture Adaptation We start the differentiable NAS process with the expanded super network directly on the target task, e.g., semantic segmentation or object detection.

Original: We ﬁrst ﬁne-tune operation weights of the super network for some epochs on the training dataset.
Revised: We ﬁrst ﬁne-tune operation weights of the super network for some epochs on the training dataset.

Original: After the weights are sufﬁciently trained, we start alternating the optimization of operation weights w with ∂ L ∂w and architecture parameters α with ∂ L ∂α .
Revised: After the weights are sufﬁciently trained, we start alternating the optimization of operation weights w with ∂ L ∂w and architecture parameters α with ∂ L ∂α .

Original: To accelerate the search process and decouple the parameters of different sub-networks, we only sample one path in each iteration according to the distribution of architecture parameters for operation weight updating.
Revised: To accelerate the search process and decouple the parameters of different sub-networks, we only sample one path in each iteration according to the distribution of architecture parameters for operation weight updating.

Original: As the search process terminates, we use the architecture parameters α to derive the target architecture.
Revised: As the search process terminates, we use the architecture parameters α to derive the target architecture.

Original: Parameters Adaptation We obtain the target architecture Arch t from architecture adaptation.
Revised: Parameter Adaptation We obtain the target architecture Arch t from architecture adaptation.

Original: To accommodate the new segmentation or detection tasks, the target architecture becomes different from that of the seed network N s (which is primitively designed for the image classiﬁcation task).
Revised: To accommodate the new segmentation or detection tasks, the target architecture becomes different from that of the seed network N s (which is primitively designed for the image classiﬁcation task).

Original: Unlike conventional training strategy, we discard the cumbersome pre-training process of Arch t on
Revised: Unlike conventional training strategy, we discard the cumbersome pre-training process of Arch t on

Original: ImageNet.
Revised: ImageNet.

Original: We remap the parameters of N s to Arch t utilizing the method described in Sec. 3.1.
Revised: We remap the parameters of N s to Arch t utilizing the method described in Sec. 3.1.

Original: Finally, we directly ﬁne-tune Arch t on the target task and obtain the ﬁnal target network N t .
Revised: Finally, we directly ﬁne-tune Arch t on the target task and obtain the ﬁnal target network N t .

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: We select the ImageNet pre-trained MobileNetV2 model as the seed network and apply our FNA method on both semantic segmentation and object detection tasks.
Revised: We select the ImageNet pre-trained MobileNetV2 model as the seed network and apply our FNA method on both semantic segmentation and object detection tasks.

Original: In this section,ﬁrstly we  give the implementation details of our experiments; then we report and analyze the network adaptation results; ﬁnally, we perform ablation studies to validate the effectiveness of the parameter-remapping paradigm and compare different parameters remapping implementations.
Revised: In this section, we ﬁrstly give the implementation details of our experiments; then we report and analyze the network adaptation results; ﬁnally, we perform ablation studies to validate the effectiveness of the parameter remapping paradigm and compare different parameter remapping implementations.

Original: 4. N ETWORK A DAPTATION ON S EMANTIC S EGMENTATION
Revised: 4. N ETWORK A DAPTATION ON S EMANTIC S EGMENTATION

Original: The semantic segmentation experiments are conducted on the Cityscapes (Cordts et al., 2016) dataset.
Revised: The semantic segmentation experiments are conducted on the Cityscapes (Cordts et al., 2016) dataset.

Original: In the architecture adaptation process, we map the seed network to the super network, which is used as the backbone of DeepLabv3 (Chen et al., 2017b).
Revised: In the architecture adaptation process, we map the seed network to the super network, which is used as the backbone of DeepLabv3 (Chen et al., 2017b).

Original: We randomly sample 20% images from the training set as the validation set for architecture parameters updating.
Revised: We randomly sample 20% images from the training set as the validation set for architecture parameters updating.

Original: The original  validation set is not used in the search process.
Revised: The original [Table] [Table] validation set is not used in the search process.

Original: To optimize the MAdds of the searched network, we deﬁne the loss function in search as L =
Revised: To optimize the MAdds of the searched network, we deﬁne the loss function in search as L =

Original: L task + λ log τ ( cost ) .
Revised: L task + λ log τ ( cost ) .

Original: The ﬁrst term denotes the crossentropy loss and the second term controls the MAdds of the network.
Revised: The ﬁrst term denotes the crossentropy loss and the second term controls the MAdds of the network.

Original: We set λ as 9 × 10 − 3 and τ as 45 .
Revised: We set λ as 9 × 10 − 3 and τ as 45 .

Original: The search process takes 80 epochs in total.
Revised: The search process takes 80 epochs in total.

Original: The architecture optimization starts after 30 epochs.
Revised: The architecture optimization starts after 30 epochs.

Original: The whole search process is conducted on a single V100 GPU and takes only 1.4 hours in total.
Revised: The whole search process is conducted on a single V100 GPU and takes only 1.4 hours in total.

Original: In the parameters adaptation process, we remap the parameters of original MobileNetV2 to the target architecture obtained in the aforementioned architecture adaptation.
Revised: In the parameter adaptation process, we remap the parameters of original MobileNetV2 to the target architecture obtained in the aforementioned architecture adaptation.

Original: The whole parameters adaptation process is conducted on 4 TITAN-Xp GPUs and takes 100 K iterations, which cost only 8 . 5 hours in total.[Table] [Table]
Revised: The whole parameter adaptation process is conducted on 4 TITAN-Xp GPUs and takes 100 K iterations, which cost only 8 . 5 hours in total.

Original: Our semantic segmentation results are shown in Tab. 1.
Revised: Our semantic segmentation results are shown in Tab. 1.

Original: The FNA network achieves 76 .
Revised: The FNA network achieves 76 .

Original: 6% mIOU on
Revised: 6% mIOU on

Original: Cityscapes with the DeepLabv3 (Chen et al., 2017b) framework, 1 . 1% mIOU better than the manually designed seed Network MobileNetV2 (Sandler et al., 2018) with fewer MAdds.
Revised: Cityscapes with the DeepLabv3 (Chen et al., 2017b) framework, 1 . 1% mIOU better than the manually designed seed Network MobileNetV2 (Sandler et al., 2018) with fewer MAdds.

Original: Compared with a NAS method DPC (Chen et al., 2018a) (with MobileNetV2 as the backbone) which searches a multi-scale module for semantic segmentation tasks, FNA gets 0 .
Revised: Compared with a NAS method DPC (Chen et al., 2018a) (with MobileNetV2 as the backbone) which searches a multi-scale module for semantic segmentation tasks, FNA gets 0 .

Original: 9% mIOU promotion with 0 .
Revised: 9% mIOU promotion with 0 .

Original: 52 B fewer MAdds.
Revised: 52 B fewer MAdds.

Original: For fair comparison with Auto-DeepLab (Liu et al., 2019a) which searches the backbone architecture on DeepLabv3 and retrains the searched network on DeepLabv3+ (Chen et al., 2018b), we adapt the parameters of the target architecture Arch t to DeepLabv3+ framework.
Revised: For fair comparison with Auto-DeepLab (Liu et al., 2019a) which searches the backbone architecture on DeepLabv3 and retrains the searched network on DeepLabv3+ (Chen et al., 2018b), we adapt the parameters of the target architecture Arch t to DeepLabv3+ framework.

Original: Comparing with Auto-DeepLab-S, FNA achieves far better mIOU with fewer MAdds, Params and training iterations.
Revised: Comparing with Auto-DeepLab-S, FNA achieves far better mIOU with fewer MAdds, Params and training iterations.

Original: With the remapping mechanism, FNA only takes 35.8 GPU hours, 1737 ×
Revised: With the remapping mechanism, FNA only takes 35.8 GPU hours, 1737 ×

Original: less than
Revised: less than

Original: DPC and 6.8 × less than Auto-DeepLab.
Revised: DPC and 6.8 × less than Auto-DeepLab.

Original: 4. N ETWORK A DAPTATION ON O BJECT D ETECTION
Revised: 4. N ETWORK A DAPTATION ON O BJECT D ETECTION

Original: We further implement our FNA method on object detection tasks.
Revised: We further implement our FNA method on object detection tasks.

Original: We adapt the MobileNetV2 seed network to two commonly used detection systems, RetinaNet (Lin et al., 2017) and a lightweight one
Revised: We adapt the MobileNetV2 seed network to two commonly used detection systems, RetinaNet (Lin et al., 2017) and a lightweight one

Original: SSDLite (Liu et al., 2016; Sandler et al., 2018).
Revised: SSDLite (Liu et al., 2016; Sandler et al., 2018).

Original: The experiments are conducted on the MS-COCO dataset (Lin et al., 2014b).
Revised: The experiments are conducted on the MS-COCO dataset (Lin et al., 2014b).

Original: Our implementation is based on the MMDetection (Chen et al., 2019a) framework.
Revised: Our implementation is based on the MMDetection (Chen et al., 2019a) framework.

Original: In the search process of architecture adaptation, we randomly sample 50% data from the original trainval35k set as the validation set.
Revised: In the search process of architecture adaptation, we randomly sample 50% data from the original trainval35k set as the validation set.

Original: We show the results on the COCO dataset in Tab. 3.
Revised: We show the results on the COCO dataset in Tab. 3.

Original: In the RetinaNet framework, compared with two manually designed networks, ShufﬂeNetV2-10 (Ma et al., 2018; Chen et al., 2019b) and MobileNetV2 (Sandler et al., 2018), FNA achieves higher mAP with similar MAdds.
Revised: In the RetinaNet framework, compared with two manually designed networks, ShufﬂeNetV2-10 (Ma et al., 2018; Chen et al., 2019b) and MobileNetV2 (Sandler et al., 2018), FNA achieves higher mAP with similar MAdds.

Original: Compared with
Revised: Compared with

Original: DetNAS (Chen et al., 2019b) which searches the backbone of detection network, FNA achieves 0 .
Revised: DetNAS (Chen et al., 2019b) which searches the backbone of detection network, FNA achieves 0 .

Original: 6% higher mAP with 1 .
Revised: 6% higher mAP with 1 .

Original: 64 M fewer Params and 0 .
Revised: 64 M fewer Params and 0 .

Original: 2 B fewer MAdds.
Revised: 2 B fewer MAdds.

Original: As shown in Tab. 4, our total computation cost is only 13.5% of DetNAS.
Revised: As shown in Tab. 4, our total computation cost is only 13.5% of DetNAS.

Original: Moreover, we implement our FNA method on the SSDLite framework.
Revised: Moreover, we implement our FNA method on the SSDLite framework.

Original: In Tab. 3, FNA surpasses both the manually designed network MobileNetV2 and the NAS-searched network MnasNet-92, where MnasNet (Tan et al., 2018) takes around 3.8K GPU days to search for the backbone network on ImageNet.
Revised: In Tab. 3, FNA surpasses both the manually designed network MobileNetV2 and the NAS-searched network MnasNet-92, where MnasNet (Tan et al., 2018) takes around 3.8K GPU days to search for the backbone network on ImageNet.

Original: The speciﬁc cost FNA takes on SSDLite is shown in Tab. 4.
Revised: The speciﬁc cost FNA takes on SSDLite is shown in Tab. 4.

Original: It is difﬁcult to train the small network due to the simpliﬁcation (Liu et al., 2019c).
Revised: It is difﬁcult to train the small network due to the simpliﬁcation (Liu et al., 2019c).

Original: Therefore, experiments on SSDLite need long training schedule and take larger computational cost.
Revised: Therefore, experiments on SSDLite need long training schedule and take larger computation cost.

Original: The experimental results further demonstrate the efﬁciency and effectiveness of direct adaptation on the target task with parameters remapping.
Revised: The experimental results further demonstrate the efﬁciency and effectiveness of direct adaptation on the target task with parameter remapping.

Original: 4. E FFECTIVENESS OF P ARAMETERS R EMAPPING
Revised: 4. E FFECTIVENESS OF P ARAMETER R EMAPPING

Original: To evaluate the effectiveness of the parameter remapping paradigm in our method, we attempt to optionally remove the parameter remapping process before the two stages, i.e. architecture adaptation and parameter adaptation.
Revised: To evaluate the effectiveness of the parameter remapping paradigm in our method [Table] we attempt to optionally remove the parameter remapping process before the two stages, i

Revised: [Table]

Revised: e. architecture adaptation and parameter adaptation.

Original: The experiments are conducted with the DeepLabv3 (Chen et al., 2017b) semantic segmentation framework on the Cityscapes dataset (Cordts et al., 2016). 
Revised: The experiments are conducted with the DeepLabv3 (Chen et al., 2017b) semantic segmentation framework on the Cityscapes dataset (Cordts et al., 2016). [Table]

Original: In Row (2) we remove the parameter remapping process before architecture adaptation.
Revised: In Row (2) we remove the parameter remapping process before architecture adaptation.

Original: In other word, the search is performed from scratch without using the pre-trained network.
Revised: In other word, the search is performed from scratch without using the pre-trained network.

Original: The mIOU in
Revised: The mIOU in

Original: Row (2) drops by 0.6% compared to FNA in Row (1).
Revised: Row (2) drops by 0.6% compared to FNA in Row (1).

Original: Then we remove the parameter remapping before parameter adaptation in Row (3), i.e. training the target architecture from scratch on the target
Revised: Then we remove the parameter remapping before parameter adaptation in Row (3), i

Original: [Table]
Revised: [Table]

Original:  task.
Revised: e. training the target architecture from scratch on the target task.

Original: The mIOU decreases by 3.6% compared the result of FNA.
Revised: The mIOU decreases by 3.6% compared the result of FNA.

Original: When we remove the parameter remapping before both stages in Row (4), it gets the worst performance.
Revised: When we remove the parameter remapping before both stages in Row (4), it gets the worst performance.

Original: In Row (5), we ﬁrst pretrain the searched architecture on ImageNet and then ﬁne-tune it on the target task.
Revised: In Row (5), we ﬁrst pretrain the searched architecture on ImageNet and then ﬁne-tune it on the target task.

Original: It is worth noting that FNA even achieves a higher mIOU by a narrow margin (0.1%) than the ImageNet pre-trained one in Row (5).
Revised: It is worth noting that FNA even achieves a higher mIOU by a narrow margin (0.1%) than the ImageNet pre-trained one in Row (5).

Original: We conjecture that this may beneﬁt from the regularization effect of parameter remapping before the parameter adaptation stage.
Revised: We conjecture that this may beneﬁt from the regularization effect of parameter remapping before the parameter adaptation stage.

Original: All the experiments are conducted using the same searching and training settings for fair comparisons.
Revised: All the experiments are conducted using the same searching and training settings for fair comparisons.

Original: With parameters remapping applied on both stages, the adaptation achieves the best results in Tab. 5.
Revised: With parameter remapping applied on both stages, the adaptation achieves the best results in Tab. 5.

Original: Especially, the remapping process before parameter adaptation tends to provide greater performance gains than the remapping before architecture adaptation.
Revised: Especially, the remapping process before parameter adaptation tends to provide greater performance gains than the remapping before architecture adaptation.

Original: All the experimental results demonstrate the importance and effectiveness of the proposed parameter remapping scheme.
Revised: All the experimental results demonstrate the importance and effectiveness of the proposed parameter remapping scheme.

Original: the same as Tab. 5.

Original: [Table]
Revised: [Table]

Original: 4. R ANDOM S EARCH E XPERIMENTS
Revised: 4. R ANDOM S EARCH E XPERIMENTS

Original: We carry out the Random Search (RandSearch) experiments with the RetinaNet (Lin et al., 2017) framework on the MS-COCO (Lin et al., 2014a) dataset.
Revised: We carry out the Random Search (RandSearch) experiments with the RetinaNet (Lin et al., 2017) framework on the MS-COCO (Lin et al., 2014a) dataset.

Original: All the results are shown in the Tab. 6.
Revised: All the results are shown in the Tab. 6.

Original: We purely replace the original differentiable NAS (DiffSearch) method in FNA with the random search method in Row (3).
Revised: We purely replace the original differentiable NAS (DiffSearch) method in FNA with the random search method in Row (3).

Revised: The random search takes the same computation cost as the search in

Revised: FNA for fair comparisons.

Original: We observe that FNA with RandSearch achieves comparable results with our original method.
Revised: We observe that FNA with RandSearch achieves comparable results with our original method.

Original: It further conﬁrms that FNA is a general framework for network adaptation and has great generalization.
Revised: It further conﬁrms that FNA is a general framework for network adaptation and has great generalization.

Original: NAS is only an implementation tool for architecture adaptation.
Revised: NAS is only an implementation tool for architecture adaptation.

Original: The whole framework of FNA can be treated as a NAS-method agnostic mechanism.
Revised: The whole framework of FNA can be treated as a NAS-method agnostic mechanism.

Original: It is worth noting that even using random search, our FNA still outperforms DetNAS (Chen et al., 2019b) with 0.2% mAP better and 150M MAdds fewer.
Revised: It is worth noting that even using random search, our FNA still outperforms DetNAS (Chen et al., 2019b) with 0.2% mAP better and 150M MAdds fewer.

Original: We further conduct similar ablation studies with experiments in Sec.
Revised: We further conduct similar ablation studies with experiments in Sec.

Original: 4.3 about the parameter remapping scheme in Row (4) - (6).
Revised: 4.3 about the parameter remapping scheme in Row (4) - (6).

Original: All the experiments further support the effectiveness of the parameter remapping scheme.
Revised: All the experiments further support the effectiveness of the parameter remapping scheme.

Original: 4. S TUDY ON P ARAMETERS R EMAPPING
Revised: 4. S TUDY ON P ARAMETER R EMAPPING

Original: We explore more strategies for the parameters remapping paradigm.
Revised: We explore more strategies for the Parameter Remapping paradigm.

Original: Similar to Sec.
Revised: Similar to Sec.

Original: 4.3, all the experiments are conducted with  DeepLabv3 (Chen et al., 2017b) framework on  Cityscapes dataset (Cordts et al., 2016).
Revised: 4.3, all the experiments are conducted with the DeepLabv3 (Chen et al., 2017b) framework on the Cityscapes dataset (Cordts et al., 2016).

Original: We make exploration from the following respects.
Revised: We make exploration from the following respects.

Original: For simplicity, we denote the weights of the seed network and the new network on the remapping dimension (output / input channel), W s =
Revised: For simplicity, we denote the weights of the seed network and the new network on the remapping dimension (output/input channel) as W s =

Original: ( W (1) s . . .
Revised: ( W (1) s . . .

Original: W ( p ) s ) and W n = ( W (1) n . . .
Revised: W ( p ) s ) and W n = ( W (1) n . . .

Original: W ( q ) n )  where q ≤ p .
Revised: W ( q ) n ) , where q ≤ p .

Original: Remapping with BN Statistics on Width-level We review the formulation of batch normalization (Ioffe & Szegedy, 2015) as follows,
Revised: Remapping with BN Statistics on Width-level We review the formulation of batch normalization (Ioffe & Szegedy, 2015) as follows,

Original: [Equation]
Revised: [Equation]

Original: where x i = ( x (1) i . . .
Revised: where x i = ( x (1) i . . .

Original: x ( p ) i ) denotes the p -dimensional input tensor of the i th layer, γ ∈ R p denotes the learnable parameter which scales the normalized data on the channel dimension.
Revised: x ( p ) i ) denotes the p -dimensional input tensor of the i th layer, γ ∈ R p denotes the learnable parameter which scales the normalized data on the channel dimension.

Original: We compute the absolute values of γ as | γ | = ( | γ (1) | . . .
Revised: We compute the absolute values of γ as | γ | = ( | γ (1) | . . .

Original: | γ ( p ) | ) .
Revised: | γ ( p ) | ) .

Original: When remapping the parameters on the width-level, we sort the values of | γ | and map the parameters with the sorted top- q indices.
Revised: When remapping the parameters on the width-level, we sort the values of | γ | and map the parameters with the sorted top- q indices.

Original: More speciﬁcally, we deﬁne a weights remapping function in Algo.
Revised: More speciﬁcally, we deﬁne a weights remapping function in Algo.

Original: 1, where the reference vector  is | γ | .
Revised: 1, where the reference vector v is | γ | .

Revised: Algorithm

Original: Input: the seed weights W s and the new weights W n , the reference vector v 1 // get indices of topk values of the vector 2 a ← topk - indices ( v , k = q ) 3 // sort the indices 4 sort ( a ) 5 for i ∈ 1 , 2 , . ..
Revised: Input : the seed weights W s and the new weights W n , the reference vector v 1 // get indices of topk values of the vector 2 a ← topk - indices ( v , k = q ) 3 // sort the indices 4 sort ( a ) 5 for i ∈ 1 , 2 , . .

Original: , q do
Revised: , q do

Original: W ( i ) n = W ( a ( i ) ) s 7 end Output: W n with remapped values
Revised: W ( i ) n = W ( a [ i ]) s 7 endOutput: W n with remapped values

Original: Remapping with Weight Importance on Width-level We attempt to utilize a canonical form of convolution weights to measure the importance of parameters.
Revised: Remapping with Weight Importance on Width-level We attempt to utilize a canonical form of convolution weights to measure the importance of parameters.

Original: Then we remap the seed network parameters with great importance to the new network.
Revised: Then we remap the seed network parameters with great importance to the new network.

Original: The remapping operation is conducted based on Algo.
Revised: The remapping operation is conducted based on Algo.

Original: 1 as well.
Revised: 1 as well.

Original: We experiment with two canonical forms of weights to compute the reference vector, the standard deviation of W s as ( std ( W (1) s ) . . .
Revised: We experiment with two canonical forms of weights to compute the reference vector, the standard deviation of W s as ( std ( W (1) s ) . . .

Original: std ( W ( p ) s )) and the L 1 norm of W s as ( || W (1) s || 1 . . .
Revised: std ( W ( p ) s )) and the L 1 norm of W s as ( || W (1) s || 1 . . .

Original: || W ( p ) s || 1 ) .
Revised: || W ( p ) s || 1 ) .

Original: [Table]
Revised: [Table]

Original: Remapping with Dilation on Kernel-level We experiment with another method of parameters remapping on  kernel-level.
Revised: Remapping with Dilation on Kernel-level We experiment with another strategy of parameter remapping on the kernel-level.

Original: Different from the function-preserving method deﬁned in Sec.
Revised: Different from the function-preserving method deﬁned in Sec.

Original: 3.1, we remap the parameters with a dilation manner as shown in Fig.
Revised: 3.1, we remap the parameters with a dilation manner as shown in Fig.

Original: The values in the convolution kernel without remapping are all assigned as 0 .
Revised: The values in the convolution kernel without remapping are all assigned as 0 .

Original: It is formulated as
Revised: It is formulated as

Original: [Equation]
Revised: [Equation]

Original: where W k × k and W 3 × 3 denote the weights of the new network and the seed network, h, w denote the spatial indices.
Revised: where W k × k and W 3 × 3 denote the weights of the new network and the seed network respectively, h, w denote the spatial indices.

Original: Tab. 7 shows the experimental results.
Revised: Tab. 7 shows the experimental results.

Original: The network adaptation with the parameter remapping paradigm deﬁne in FNA achieves the best results.
Revised: The network adaptation with the parameter remapping paradigm deﬁne in FNA achieves the best results.

Original: Furthermore, the remapping operation of FNA is easier to implement compared to the several aforementioned ones.
Revised: Furthermore, the remapping operation of FNA is easier to implement compared to the several aforementioned ones.

Original: However, we explore limited numbers of methods to implement the parameter remapping paradigm.
Revised: However, we explore limited number of methods to implement the parameter remapping paradigm.

Original: How to conduct the remapping strategy  remainsto be a signiﬁcative future work.
Revised: How to conduct the remapping strategy more efﬁciently remains a signiﬁcative future work.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this paper, we propose a fast neural network adaptation method (FNA) with a parameter remapping paradigm.
Revised: In this paper, we propose a fast neural network adaptation method (FNA) with a parameter remapping paradigm and the architecture search method.

Original: We adapt the manually designed network MobileNetV2 to semantic segmentation and detection tasks on both architecture- and parameter- level.
Revised: We adapt the manually designed network MobileNetV2 to semantic segmentation and detection tasks on both architecture- and parameter- level.

Original: The parameter-remapping strategy takes full advantages of the seed network parameters, which  accelerates both the architecture search and parameter ﬁne-tuning process greatly.
Revised: The parameter remapping strategy takes full advantages of the seed network parameters, which greatly accelerates both the architecture search and parameter ﬁne-tuning process.

Original: With our FNA method, researchers and engineers could fast adapt more manually designed networks to various frameworks on different tasks.
Revised: With our FNA method, researchers and engineers could fast adapt more manually designed networks to various frameworks on different tasks.

Original: As there are lots of ImageNet pre-trained models available in the community, we could conduct adaptation with low cost and do more applications, e.g., face recognition, pose estimation, depth estimation, etc.
Revised: As there are lots of ImageNet pre-trained models available in the community, we could conduct adaptation with low cost and do more applications, e.g., face recognition, pose estimation, depth estimation, etc.

Original: We leave more efﬁcient remapping strategies and more applications for future work.
Revised: We leave more efﬁcient remapping strategies and more applications for future work.

Revised: A CKNOWLEDGEMENT

Revised: This work was supported by National Natural Science Foundation of China (NSFC) (No. 61876212,

Revised: No. 61733007 and No. 61572207), National Key R&D Program of China (No. 2018YFB1402600) and HUST-Horizon Computer Vision Research Center.

Revised: We thank Liangchen Song, Yingqing Rao and Jiapei Feng for the discussion and assistance.

Original: A A PPENDIX A. I MPLEMENTATION D ETAILS ON S EMANTIC S EGMENTATION
Revised: A A PPENDIX A. I MPLEMENTATION D ETAILS ON S EMANTIC S EGMENTATION

Original: For architecture adaptation, the image is ﬁrst resized to 512 × 1024 and 321 × 321 patches are randomly cropped as the input data.
Revised: For architecture adaptation, the image is ﬁrst resized to 512 × 1024 and 321 × 321 patches are randomly cropped as the input data.

Original: The output feature maps are down-sampled by the factor of 16 .
Revised: The output feature maps are down-sampled by the factor of 16 .

Original: Depthwise-separable convolution is used in the ASPP module (Chen et al., 2017a;b).
Revised: Depthwise separable convolutions are used in the ASPP module (Chen et al., 2017a;b).

Original: The stages where the expansion ratio of MBConv is 6 in the original MobileNetV2 are searched and adjusted.
Revised: The stages where the expansion ratio of MBConv is 6 in the original MobileNetV2 are searched and adjusted.

Original: We set the  numbers of layers in each searched stage of the super network as { 4 , 4 , 6 , 6 , 4 , 1 } .
Revised: We set the maximum numbers of layers in each searched stage of the super network as { 4 , 4 , 6 , 6 , 4 , 1 } .

Original: We set a warm-up stage in the ﬁrst 5 epochs to linearly increase the learning rate from 1 × 10 − 4 to 0 .
Revised: We set a warm-up stage in the ﬁrst 5 epochs to linearly increase the learning rate from 1 × 10 − 4 to 0 .

Original: Then, the learning rate decays to 1 × 10 − 3 with the cosine annealing schedule (Loshchilov & Hutter, 2017).
Revised: Then, the learning rate decays to 1 × 10 − 3 with the cosine annealing schedule (Loshchilov & Hutter, 2017).

Original: The batch size is set as 16 .
Revised: The batch size is set as 16 .

Original: We use the SGD optimizer with 0 .
Revised: We use the SGD optimizer with 0 .

Original: 9 momentum and 5 × 10 −weight decay for operation weights and the Adam optimizer (Kingma & Ba, 2015) with 4 × 10 −weight decay and a ﬁxed learning rate 1 × 10 − 3 .
Revised: 9 momentum and 5 × 10 − 4 weight decay for operation weights and the Adam optimizer (Kingma & Ba, 2015) with 4 × 10 − 5 weight decay and a ﬁxed learning rate 1 × 10 − 3 for architecture parameters.

Original: For parameter adaptation, the training data is cropped as a 769 × 769 patch from the rescaled image.
Revised: For parameter adaptation, the training data is cropped as a 769 × 769 patch from the rescaled image.

Original: The scale is randomly selected from [0 . 75 , 1 . 0 , 1 . 25 , 1 . 5 , 1 . 75 , 2 . 0] .
Revised: The scale is randomly selected from [0 . 75 , 1 . 0 , 1 . 25 , 1 . 5 , 1 . 75 , 2 . 0] .

Original: The random left-right ﬂipping is used.
Revised: The random left-right ﬂipping is used.

Original: We update the statistics of the batch normalization (BN) (Ioffe & Szegedy, 2015) foriterations before the ﬁne-tuning process.
Revised: We update the statistics of the batch normalization (BN) (Ioffe & Szegedy, 2015) foriterations before the ﬁne-tuning process.

Original: We use the same SGD optimizer as the search process.
Revised: We use the same SGD optimizer as the search process.

Original: The learning rate linearly increases from 1 × 10 − 4 to 0 .
Revised: The learning rate linearly increases from 1 × 10 − 4 to 0 .

Original: and then decays to 0 with the polynomial schedule.
Revised: and then decays to 0 with the polynomial schedule.

Original: The batch size is set as 16 .
Revised: The batch size is set as 16 .

Original: A. I MPLEMENTATION D ETAILS ON O BJECT D ETECTION
Revised: A. I MPLEMENTATION D ETAILS ON O BJECT D ETECTION

Original: RetinaNet We describe the details in the search process of architecture adaptation.
Revised: RetinaNet We describe the details in the search process of architecture adaptation as follows.

Original: The depth settings in each searched stage are set as { 4 , 4 , 4 , 4 , 4 , 1 } .
Revised: The depth settings in each searched stage are set as { 4 , 4 , 4 , 4 , 4 , 1 } .

Original: For the input image size, the short side is resized to 800 while the maximum long side is set as 1333.
Revised: For the input image size, the short side is resized to 800 while the maximum long side is set as 1333.

Original: For operation weights, we use the SGD optimizer with 1 × 10 − 4 weight decay and 0 .
Revised: For operation weights, we use the SGD optimizer with 1 × 10 − 4 weight decay and 0 .

Original: 9 momentum.
Revised: 9 momentum.

Original: We set a warm-up stage in the ﬁrst 500 iterations to linearly increase the learning rate from 0 to 0 .
Revised: We set a warm-up stage in the ﬁrst 500 iterations to linearly increase the learning rate from 0 to 0 .

Original: Then we decay the learning rate by a factor of 0 .
Revised: Then we decay the learning rate by a factor of 0 .

Original: 1 at 8 and 11 epochs.
Revised: 1 at the 8th and 11th epoch.

Original: For the architecture parameters, we use the Adam optimizer (Kingma & Ba, 2015) with 1 × 10 − 3 weight decay and a ﬁxed learning rate 3 × 10 − 4 .
Revised: For the architecture parameters, we use the Adam optimizer (Kingma & Ba, 2015) with 1 × 10 − 3 weight decay and a ﬁxed learning rate 3 × 10 − 4 .

Original: For the multi-objective loss function, we set λ as 0 .
Revised: For the multi-objective loss function, we set λ as 0 .

Original: 02 and τ as 10 .
Revised: 02 and τ as 10 .

Original: We begin optimizing the architecture parameters at 8 epochs.
Revised: We begin optimizing the architecture parameters after 8 epochs.

Original: We remove the random ﬂipping operation on input images in the search process.
Revised: We remove the random ﬂipping operation on input images in the search process.

Original: All the other training settings are the same as MMDetection (Chen et al., 2019a) implementation.
Revised: All the other training settings are the same as MMDetection (Chen et al., 2019a) implementation.

Original: For ﬁne-tuning of the parameter adaptation, we use the SGD optimizer with 5 × 10 − 5 weight decay and 0.9 momentum.
Revised: For ﬁne-tuning of the parameter adaptation, we use the SGD optimizer with 5 × 10 − 5 weight decay and 0.9 momentum.

Original: A similar warm-up procedure is set in the ﬁrst 500 iterations to increase the learning rate from 0 to 0 .
Revised: A similar warm-up procedure is set in the ﬁrst 500 iterations to increase the learning rate from 0 to 0 .

Original: Then we decay the learning rate by 0 .
Revised: Then we decay the learning rate by 0 .

Original: 1 at the 8 and 11 epochs.
Revised: 1 at the 8th and 11th epoch.

Original: The whole architecture search process takes 14 epochs, 18 hours on 8 TITAN-Xp GPUs  and the whole parameters ﬁne-tuning takes  10 hours on 8 TITAN-Xp GPUs.
Revised: The whole architecture search process takes 14 epochs, 18 hours on 8 TITAN-Xp GPUs with the batch size of 8 and the whole parameter ﬁne-tuning takes 12 epochs, 10 hours on 8 TITAN-Xp GPUs with 32 batch size.

Original: SSDLite We resize the input images to 320 × 320 .
Revised: SSDLite We resize the input images to 320 × 320 .

Original: For operation weights in the search process, we use the standard RMSProp optimizer with 4 × 10 − 5 weight decay.
Revised: For operation weights in the search process, we use the standard RMSProp optimizer with 4 × 10 − 5 weight decay.

Original: The warm-up stage in the ﬁrst 500 iterations increases learning rate from 0 to 0 .
Revised: The warm-up stage in the ﬁrst 500 iterations increases learning rate from 0 to 0 .

Original: Then we decay the learning rate by 0 .
Revised: Then we decay the learning rate by 0 .

Original: 1 at 16 and 22 epochs.
Revised: 1 at 16 and 22 epochs.

Original: The architecture optimization starts at 12 epochs.
Revised: The architecture optimization starts at 12 epochs.

Original: We set λ as 0 . 2 and τ as 10 for the loss function.
Revised: We set λ as 0 . 2 and τ as 10 for the loss function.

Original: The other search settings are the same as the RetinaNet experiment.
Revised: The other search settings are the same as the RetinaNet experiment.

Original: For parameters adaptation, the initial learning rate is 0 . 2 and decays at 36, 50 and 56 epochs.
Revised: For parameter adaptation, the initial learning rate is 0 . 2 and decays at 36, 50 and 56 epochs.

Original: The training settings follow the SSD (Liu et al., 2016) implementation in MMDetection (Chen et al.,2019a).
Revised: The training settings follow the SSD (Liu et al., 2016) implementation in MMDetection (Chen et al.,

Original: The search process takes 24 epochs in total, 20 hours on 8 TITAN-Xp GPUs.

Original: The parameters adaptation takes 60 epochs, 46 hours on 8 TITAN-Xp GPUs.

Original: M B K 3 _ E

Original: M B K 7 _ E

Original: M B K 7 _ E

Original: M B K 5 _ E

Original: M B K 7 _ E

Original: M B K 7 _ E

Original: M B K 7 _ E

Original: M B K 7 _ E

Original: M B K 7 _ E

Original: M B K 5 _ E

Original: M B K 7 _ E

Original: M B K 3 _ E

Original: C o n v 3 ×

Original: M B K 3 _ E

Original: M B K 3 _ E

Original: M B K 7 _ E

Original: M B K 3 _ E

Original: M B K 3 _ E

Original: M B K 5 _ E

Original: M B K 7 _ E

Original: M B K 7 _ E         (

Original: [Table]
Revised: [Table]

Original: ) FNA on DeepLabv

Original: M B K_ E

Original: C o n v 3 ×

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E

Original: M B K_ E        (b) FNA on RetinaNet

Original: M B K 3 _

Original: E

Original: C o n v 3 ×

Original: M B K 5 _
Revised: M B K 5 _

Original: E
Revised: E

Original: M B K 5 _
Revised: M B K 5 _

Original: E
Revised: E

Original: M B K 5 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 3 _
Revised: M B K 3 _

Original: E
Revised: E

Original: M B K 5 _

Original: E

Original: M B K 5 _

Original: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 3 _

Original: E

Original: M B K 7 _

Original: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 5 _
Revised: M B K 5 _

Original: E
Revised: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: M B K 7 _
Revised: M B K 7 _

Original: E
Revised: E

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure]

Original: [Figure]