A popular method to reduce the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all follow a common theme: normalize across spatial dimensions and discard the extracted statistics. In this paper, we propose an alternative normalization method that noticeably departs from this convention and normalizes exclusively across channels. We argue that the channel dimension is naturally appealing as it allows us to extract the first and second moments of features extracted at a particular image position. These moments capture structural information about the input image and extracted features, which opens a new avenue along which a network can benefit from feature normalization: Instead of disregarding the normalization constants, we propose to re-inject them into later layers to preserve or transfer structural information in generative networks. Codes are available at https://github.com/Boyiliee/PONO.
A key innovation that enabled the undeniable success of deep learning is the internal normalization of activations. Although normalizing inputs had always been one of the “tricks of the trade” for training neural networks (LeCun et al., 2012), batch normalization (BN) (Ioffe and Szegedy, 2015) extended this practice to every layer, which turned out to have crucial benefits for deep networks. While the success of normalization methods was initially attributed to “reducing internal covariate shift” in hidden layers (Ioffe and Szegedy, 2015; Lei Ba et al., 2016), an array of recent studies (Balduzzi et al., 2017; van Laarhoven, 2017; Santurkar et al., 2018; Bjorck et al., 2018; Zhang et al., 2019; Hoffer et al., 2018; Luo et al., 2019; Arora et al., 2019) has provided evidence that BN changes the loss surface and prevents divergence even with large step sizes (Bjorck et al., 2018), which accelerates training (Ioffe and Szegedy, 2015).
Multiple normalization schemes have been proposed, each with its own set of advantages: Batch normalization (Ioffe and Szegedy, 2015) benefits training of deep networks primarily in computer vision tasks. Group normalization (Wu and He, 2018) is often the first choice for small mini-batch settings such as object detection and instance segmentation tasks. Layer Normalization (Lei Ba et al., 2016) is well suited to sequence models, common in natural language processing. Instance normalization (Ulyanov et al., 2016) is widely used in image synthesis owing to its apparent ability to remove style information from the inputs. However, all aforementioned normalization schemes follow a common theme: they normalize across spatial dimensions and discard the extracted statistics. The philosophy behind their design is that the first two moments are considered expendable and should be removed.
In this paper, we introduce Positional Normalization (PONO), which normalizes the activations at each position independently across the channels. The extracted mean and standard deviation capture the coarse structural information of an input image (see Figure 1). Although removing the first two moments does benefit training, it also eliminates important information about the image, which — in the case of a generative model — would have to be painfully relearned in the decoder. Instead, we propose to bypass and inject the two moments into a later layer of the network, which we refer to as Moment Shortcut (MS) connection.
PONO is complementary to previously proposed normalization methods (such as BN) and as such can and should be applied jointly. We provide evidence that PONO has the potential to substantially enhance the performance of generative models and can exhibit favorable stability throughout the training procedure in comparison with other methods. PONO is designed to deal with spatial information, primarily targeted at generative (Goodfellow et al., 2014; Isola et al., 2017) and sequential models (Sutskever et al., 2014; Karpathy et al., 2014; Hochreiter and Schmidhuber, 1997; Rumelhart et al., 1986). We explore the benefits of PONO with MS in several initial experiments across different model architectures and image generation tasks and provide code online at https://github.com/Boyiliee/PONO.
2 Related Work
Normalization is generally applied to improve convergence speed during training (Orr and Müller, 2003). Normalization methods for neural networks can be roughly categorized into two regimes: normalization of weights (Salimans and Kingma, 2016; Miyato et al., 2018; Wu et al., 2019; Qiao et al., 2019) and normalization of activations (Ioffe and Szegedy, 2015; Lei Ba et al., 2016; Wu and He, 2018; Lyu and Simoncelli, 2008; Jarrett et al., 2009; Krizhevsky et al., 2012; Ulyanov et al., 2016; Luo et al., 2018; Shao et al., 2019). In this work, we focus on the latter.
Given the activations (where denotes the batch size, the number of channels, the height, and the width) in a given layer of a neural net, the normalization methods differ in the dimensions over which they compute the mean and variance, see Figure 2. In general, activation normalization methods compute the mean and standard deviation (std) of the features in their own manner, normalize the features with these statistics, and optionally apply an affine transformation with parameters (new mean) and (new std). This can be written as
Batch Normalization (BN) (Ioffe and Szegedy, 2015) computes and across the B, H, and W dimensions. BN increases the robustness of the network with respect to high learning rates and weight initializations (Bjorck et al., 2018), which in turn drastically improves the convergence rate. Synchronized Batch Normalization treats features of mini-batches across multiple GPUs like a single mini-batch. Instance Normalization (IN) (Ulyanov et al., 2016) treats each instance in a mini-batch independently and computes the statistics across only spatial dimensions (H and W). IN aims to make a small change in the stylization architecture results in a significant qualitative improvement in the generated images. Layer Normalization (LN) normalizes all features of an instance within a layer jointly, i.e., calculating the statistics over the C, H, and W dimensions. LN is beneficial in natural language processing applications (Lei Ba et al., 2016; Vaswani et al., 2017). Notably, none of the aforementioned methods normalize the information at different spatial position independently. This limitation gives rise to our proposed Positional Normalization.
Batch Normalization introduces two learned parameters and to allow the model to adjust the mean and std of the post-normalized features. Specifically, are channel-wise parameters. Conditional instance normalization (CIN) (Dumoulin et al., 2017) keeps a set parameter of pairs which enables the model to have different behaviors conditioned on a style class label . Adaptive instance normalization (AdaIN) (Huang and Belongie, 2017) generalizes this to an infinite number of styles by using the and of IN borrowed from another image as the and . Dynamic Layer Normalization (DLN) (Kim et al., 2017) relies on a neural network to generate the and . Later works (Huang et al., 2018; Karras et al., 2018) refine AdaIN and generate the and of AdaIN dynamically using a dedicated neural network. Conditional batch normalization (CBN) (De Vries et al., 2017) follows a similar spirit and uses a neural network that takes text as input to predict the residual of and , which is shown to be beneficial to visual question answering models.
Notably, all aforementioned methods generate and as vectors, shared across spatial positions. In contrast, Spatially Adaptive Denormalization (SPADE) (Park et al., 2019), an extension of Synchronized Batch Normalization with dynamically predicted weights, generates the spatially dependent using a two-layer ConvNet with raw images as inputs.
Finally, we introduce shortcut connections to transfer the first and second moment from early to later layers. Similar skip connections (with add, concat operations) have been introduced in ResNets (He et al., 2016) and DenseNets (Huang et al., 2017) and earlier works (Bishop, 1995; Hochreiter and Schmidhuber, 1997; Ripley, 2007; Srivastava et al., 2015; Kim et al., 2016), and are highly effective at improving network optimization and convergence properties (Li et al., 2018b).
3 Positional Normalization and Moment Shortcut
Prior work has shown that feature normalization has a strong beneficial effect on the convergence behavior of neural networks (Bjorck et al., 2018). Although we agree with these findings, in this paper we claim that removing the first and second order information at multiple stages throughout the network may also deprive the deep net of potentially useful information — particularly in the context of generative models, where a plausible image needs to be generated.
Our normalization scheme, which we refer to as Positional Normalization (PONO), differs from prior work in that we normalize exclusively over the channels at any given fixed pixel location (see Figure 2). Consequently, the extracted statistics are position dependent and reveal structural information at this particular layer of the deep net. The mean can be considered itself an “image”, where the intensity of pixel represents the average activation at this particular image location in this layer. The standard deviation is the natural second order extension. Formally, PONO computes
where is a small stability constant (e.g., ) to avoid divisions by zero and imaginary values due to numerical inaccuracies.
As PONO computes the normalization statistics at all spatial positions independently from each other (unlike BN, LN, CN, and GN) it is translation, scaling, and rotation invariant. Further, it is complementary to existing normalization methods and, as such, can be readily applied in combination with e.g. BN.
As the extracted mean and standard deviations are themselves images, we can visualize them to obtain information about the extract features at the various layers of a convolutional network. Such visualizations can be revealing and could potentially be used to debug or improve network architectures. Figure 1 shows heat-maps of the and captured by PONO at several layers (Conv1_2, Conv2_2, Conv3_4, and Conv4_4) of VGG-19 (Simonyan and Zisserman, 2015). The figure reveals that the features in lower layers capture the silhouette of a cat while higher layers locate the position of noses, eyes, and the end points of ears —- suggesting that later layers may focus on higher level concepts corresponding to essential facial features (eyes, nose, mouth), whereas earlier layers predominantly extract generic low level features like edges. We also observe a similar phenomenon from the features of ResNets (He et al., 2016) and DenseNets (Huang et al., 2017) (see Figure 3 and Appendix). The resulting images are reminiscent of related statistics captured in texture synthesis (Freeman and Adelson, 1991; Osada et al., 2002; Dryden, 2014; Efros and Leung, 1999; Efros and Freeman, 2001; Heeger and Bergen, 1995; Wei and Levoy, 2000). We observe that unlike VGG and ResNet, DenseNet exhibits strange behavior on corners and boundaries which may degrade performance when fine-tuned on tasks requiring spatial information such as object detection or segmentation. This suggests that the padding and downsampling procedure of DenseNet should be revisited and may lead to improvements if fixed, see Figure 3. The visualizations of the PONO statistics support our hypothesis that the mean and the standard deviation may indeed capture structural information of the image and extracted features, similar to the way statistics computed by IN have the tendency to capture aspects of the style of the input image (Ulyanov et al., 2016; Huang and Belongie, 2017). This extraction of valuable information motivates the Moment Shortcut described in the subsequent section.
3.1 Moment Shortcut
In generative models, a deep net is trained to generate an output image from some inputs (images). Typically, generative models follow an encoder-decoder architecture, where the encoder digests an image into a condensed form and the decoder recovers a plausible image with some desired properties. For example, Huang et al. (Huang and Belongie, 2017) try to transfer the style from an image A to an image B, Zhu et al. (Zhu et al., 2017) “translate” an image from an input distribution (e.g., images of zebras) to an output distribution (e.g., images of horses), Choi et al. (Choi et al., 2018) use a shared encoder-decoder with a classification loss in the encoded latent space to enable translation across multiple distributions, and (Huang et al., 2018; Lee et al., 2018) combine the structural information of an image with the attributes from another image to generate a fused output.
U-Nets (Ronneberger et al., 2015) famously achieve strong results and compelling optimization properties in generative models through the introduction of skip connections from the encoder to the decoder. PONO gives rise to an interesting variant of such skip connections. Instead of connecting all channels, we only “fast-forward” the positional moment information and extracted from earlier layers. We refer to this approach as Moment Shortcut (MS).
Figure 4 (left) illustrates the use of MS in the context of an autoencoder. Here, we extract the first two moments of the activations () in an encoder layer, and send them to a corresponding decoder layer. Importantly, the mean is added in the encoder, and the std is multiplied, similar to in the standard BN layer. To be specific, , where is modeled by the intermediate layers, and the and are the and extracted from the input . MS biases the decoder explicitly so that the activations in the decoder layers give rise to similar statistics than corresponding layers in the encoder. As MS shortcut connections can be used with and without normalization, we refer to the combination of PONO with MS as PONO-MS throughout.
Provided PONO does capture essential structural signatures from the input images, we can use the extracted moments to transfer this information from a source to a target image. This opens an opportunity to go beyond autoencoders and use PONO-MS in image-to-image translation settings, for example in the context of CycleGAN (Zhu et al., 2017) and Pix2Pix (Isola et al., 2017). Here, we transfer the structure (through and ) of one image from the encoder to the decoder of another image.
Dynamic Moment Shortcut.
Inspired by Dynamic Layer Normalization and similar works (Kim et al., 2017; Huang et al., 2018; Karras et al., 2018; Chen et al., 2018a; Park et al., 2019), we propose a natural extension called Dynamic Moment Shortcut (DMS): instead of re-injecting and as is, we use a convolutional neural network that takes and as inputs to generate the and for MS. This network can either generate one-channel outputs or multi-channel outputs (like (Park et al., 2019)). The right part of Figure 4 illustrates DMS with one-channel output. DMS is particularly helpful when the task involves shape deformation or distortion. We refer to this approach as PONO-DMS in the following sections. In our experiments, we explore using a ConvNet with either one or two layers.
4 Experiments and Analysis
We conduct our experiments on unpaired and paired image translation tasks using CycleGAN (Zhu et al., 2017) and Pix2pix (Isola et al., 2017) as baselines, respectively. Our code is available at https://github.com/Boyiliee/PONO.
4.1 Experimental Setup
We follow the same setup as CycleGAN (Zhu et al., 2017) and Pix2pix (Isola et al., 2017) using their official code base.
We use two evaluation metrics, as follows.
(1) Fréchet Inception Distance (Heusel et al., 2017) between the output images and all test images in the target domain. FID uses an Inception (Szegedy et al., 2015) model pretrained on ImageNet (Deng et al., 2009) to extract image features. Based on the means and covariance matrices of the two sets of extracted features, FID is able to estimate how different two distributions are.
(2) Average Learned Perceptual Image Patch Similarity distance (Zhang et al., 2018) of all output and target image pairs. LPIPS is based on pretrained AlexNet (Krizhevsky et al., 2012) features
We include four baseline approaches: (1) CycleGAN or Pix2pix baselines; (2) these baselines with SPADE (Park et al., 2019), which passes the input image through a 2-layer ConvNet and generates the and for BN in the decoder. (3) the baseline with additive skip connections where encoder activations are added to decoder activations; (4) the baseline with concatenated skip connections, where encoder activations are concatenated to decoder activations as additional channels (similar to U-Nets (Ronneberger et al., 2015)). For all models, we follow the same setup as CycleGAN (Zhu et al., 2017) and Pix2pix (Isola et al., 2017) using their implementations. Throughout we use the hyper-parameters suggested by the original authors.
4.2 Comparison against Baselines
We add PONO-MS and PONO-DMS to the CycleGAN generator; see the Appendix for the model architecture. Table 1 shows that both cases outperform all baselines at transforming maps into photos, with the only exception of SPADE (which however performs worse in the other direction).
Although skip connections could help make up for the lost information, we postulate that directly adding the intermediate features back may introduce too much unnecessary information and might distract the model. Unlike the skip connections, SPADE uses the input to predict the parameters for normalization. However, on Photo Map, the model has to learn to compress the input photos and extract structural information from it. A re-introduction of the original raw input may disturb this process and explain the worse performance. In contrast, PONO-MS normalizes exclusively across channels which allows us to capture structural information of a particular input image and re-inject/transfer it to later layers.
|Map Photo||Photo Map||Horse Zebra||Zebra Horse|
|Method||# of param.||FID||FID||FID||FID|
The Pix2pix model (Isola et al., 2017) is a conditional adversarial network introduced as a general-purpose solution for image-to-image translation problems. Here we conduct experiments on whether PONO-MS helps Pix2pix (Isola et al., 2017) with Maps (Zhu et al., 2017), Cityscapes (Cordts et al., 2016) and Day Night (Laffont et al., 2014). We train for 200 epochs and compare the results with/without PONO-MS, under similar conditions with matching number of parameters. Results are summarized in Table 2.
|Maps (Zhu et al., 2017)||Cityscapes (Cordts et al., 2016)||Day Night (Laffont et al., 2014)|
|Map Photo||Photo Map||SL Photo||Photo SL||Day Night||Night Day|
|Pix2pix (Baseline)||60.07 / 0.333||68.73 / 0.169||71.24 / 0.422||102.38 / 0.223||196.58 / 0.608||131.94 / 0.531|
|+PONO-MS||56.88 / 0.333||68.57 / 0.166||60.40 / 0.331||97.78 / 0.224||191.10 / 0.588||131.83 / 0.534|
4.3 Ablation Study
Table 3 contains the results of several experiments to evaluate the sensitivities and design choices of PONO-MS and PONO-DMS. Further, we evaluate Moment Shortcut (MS) without PONO, where we bypass both statistics, and , without normalizing the features. The results indicate that PONO-MS outperforms MS alone, which suggests that normalizing activations with PONO is beneficial. PONO-DMS can lead to further improvements, and some settings (e.g. 1 conv 3 3, multi-channel) consistently outperform PONO-MS. Here, multi-channel predictions are clearly superior over single-channel predictions but we do not observe consistent improvements from a rather than a kernel size.
|Method||Map Photo||Photo Map||Horse Zebra||Zebra Horse|
|+Moment Shortcut (MS)||54.5||56.6||79.8||146.1|
|+PONO-DMS (1 conv , one-channel)||55.1||53.8||74.1||147.2|
|+PONO-DMS (2 conv , one-channel)||56.0||53.3||81.6||144.8|
|+PONO-DMS (1 conv , multi-channel)||53.7||54.1||65.7||140.6|
|+PONO-DMS (2 conv , multi-channel)||52.7||54.7||64.9||155.2|
|+PONO-DMS (2 conv , multi-channel)||48.9||57.3||74.3||148.4|
|+PONO-DMS (2 conv , multi-channel)||50.3||51.4||72.2||146.1|
Unlike previous normalization methods such as BN and GN that emphasize on accelerating and stabilizing the training of networks, PONO is used to split off part of the spatial information and re-inject it later. Therefore, PONO-MS can be applied jointly with other normalization methods. In Table 4 we evaluate four normalization approaches (BN, IN, LN, GN) with and without PONO-MS, and PONO-MS without any additional normalization (bottom row). In detail, BN + PONO-MS is simply applying PONO-MS to the baseline model and keep the original BN modules which have a different purpose: to stabilize and speed up the training. We also show the models where BN is replaced by LN/IN/GN as well as these models with PONO-MS. The last row shows PONO-MS can work independently when we remove the original BN in the model. Each table entry displays the FID score without and with PONO-MS (the lower score is in bold). The final column (very right) contains the average improvement across all four tasks, relative to the default architecture, BN without PONO-MS. Two clear trends emerge: 1. All four normalization methods improve with PONO-MS on average and on almost all individual tasks; 2. additional normalization is clearly beneficial over pure PONO-MS (bottom row).
|Method||Map Photo||Photo Map||Horse Zebra||Zebra Horse||Avg. Improvement|
|BN (Default) / BN + PONO-MS||57.92 / 52.81||58.32 / 53.23||86.28 / 71.18||155.91 / 142.21||1 / 0.890|
|IN / IN + PONO-MS||67.87 / 47.14||57.93 / 54.18||67.85 / 69.21||154.15 / 153.61||0.985 / 0.883|
|LN / LN + PONO-MS||54.84 / 49.81||53.00 / 50.08||87.26 / 67.63||154.49 / 142.05||0.964 / 0.853|
|GN / GN + PONO-MS||51.31 / 50.12||50.62 / 50.50||93.58 / 63.53||143.56 / 144.99||0.940 / 0.849|
5 Further Analysis and Explorations
In this section, we apply PONO-MS to two state-of-the-art unsupervised image-to-image translation models: MUNIT (Huang et al., 2018) and DRIT (Lee et al., 2018). Both approaches may arguably be considered concurrent works and share a similar design philosophy. Both aim to translate an image from a source to a target domain, while imposing the attributes (or the style) of another target domain image.
As task, we are provided with an image in source domain A and an image in target domain B. DRIT uses two encoders, one to extract content features from , and the other to extract attribute features from . A decoder then takes and as inputs to generate the output image . MUNIT follows a similar pipeline. Both approaches are trained on the two directions, and , simultaneously. We apply PONO to DRIT or MUNIT immediately after the first three convolution layers (convolution layers before the residual blocks) of the content encoders. We then use MS before the last three transposed convolution layers with matching decoder sizes. We follow the DRIT and MUNIT frameworks and consider the extracted statistics (’s and ’s) as part of the content tensors.
5.1 Experimental Setup
We consider two datasets provided by the authors of DRIT: 1) Portrait Photo (Lee et al., 2018; Liu et al., 2015) with 1714 painting images and 6352 human photos for training, and 100 images in each domain for testing and 2) Cat Dog (Lee et al., 2018) containing 771 cat images and 1264 dog images for training, and 100 images in each domain for testing.
In the following experiments, we use the official codebases
5.2 Results of Attribute Controlled Image Translation
Figure 5 shows the qualitative results on the Cat Dog dataset. (Here we show the results of MUNIT’ + PONO-MS which will be explained later.) We observe a clear trend that PONO-MS helps these two models obtain more plausible results. We observe the models with PONO-MS is able to capture the content features and attributes distributions, which motivates baseline models to digest different information from both domains. For example, in the first row, when translating cat to dog, DRIT with PONO-MS is able to capture the cat’s facial expression, and MUNIT with PONO-MS could successfully generate dog images with plausible content, which largely boosts the performance of the baseline models. More qualitative results of randomly selected inputs are provided in the Appendix.
Table 5 show the quantitative results on both Cat Dog and Portrait Photo datasets. PONO-MS improves the performance of both models on all instance-level metrics (LPIPS\textsubscriptattr, LPIPS\textsubscriptcont, and VGG loss). However, the dataset-level metric, FID, doesn’t improve too much. We believe the reason is that FID is calculated based on the first two order statistic of Inception features and may discard some subtle differences between each output pair.
|Portrait Photo||Portrait Photo|
|DRIT + PONO-MS||127.9||0.534||0.457||1.744||99.5||0.575||0.463||2.022|
|MUNIT + PONO-MS||270.5||0.541||0.423||1.559||127.5||0.586||0.477||2.202|
|MUNIT’ + PONO-MS||159.4||0.424||0.319||1.324||125.1||0.566||0.312||1.824|
|Cat Dog||Cat Dog|
|DRIT + PONO-MS||47.5||0.524||0.576||2.147||41.0||0.514||0.604||2.003|
|MUNIT + PONO-MS||254.8||0.632||0.501||1.614||276.2||0.624||0.585||2.119|
|MUNIT’ + PONO-MS||80.4||0.615||0.406||1.610||90.8||0.477||0.428||1.689|
Interestingly MUNIT, while being larger than DRIT (30M parameters vs. 10M parameters), doesn’t perform better on these two datasets. One reason for its relatively poor performance could be that the model was not designed for these datasets (MUNIT uses a much larger unpublished dogs to big cats dataset), the dataset are very small, and the default image resolution is slightly different. To further improve MUNIT + PONO-MS, we add one more Conv3x3-LN-ReLU layer before the output layer. Without this, there is only one layer between the outputs and the last re-introduced and . Therefore, adding one additional layer allows the model to learn a nonlinear function of these and . We call this model MUNIT’ + PONO-MS. Adding this additional layer significantly enhances the performance of MUNIT while introducing only 75K parameters (about 0.2%). We also provide the numbers of MUNIT’ (MUNIT with one additional layer) as a baseline for a fair comparison.
Admittedly, the state-of-the-art generative models employ complex architecture and a variety of loss functions; therefore, unveiling the full potential of PONO-MS on these models can be nontrivial and required further explorations. It is fair to admit that the results of all model variations are still largely unsatisfactory and the image translation task remains an open research problem.
However, we hope that our experiments on DRIT and MUNIT may shed some light on the potential value of PONO-MS, which could open new interesting directions of research for neural architecture design.
6 Conclusion and Future Work
In this paper, we propose a novel normalization technique, Positional Normalization (PONO), in combination with a purposely limited variant of shortcut connections, Moment Shortcut (MS). When applied to various generative models, we observe that the resulting model is able to preserve structural aspects of the input, improving the plausibility performance according to established metrics. PONO and MS can be implemented in a few lines of code (see Appendix). Similar to Instance Normalization, which has been observed to capture the style of image (Huang and Belongie, 2017; Karras et al., 2018; Ulyanov et al., 2016), Positional Normalization captures structural information. As future work we plan to further explore such disentangling of structural and style information in the design of modern neural architectures.
It is possible that PONO and MS can be applied to a variety of tasks such as image segmentation (Long et al., 2015; Ronneberger et al., 2015), denoising (Xie et al., 2012; Li et al., 2017), inpainting (Yu et al., 2018), super-resolution (Dong et al., 2014), and structured output prediction (Sohn et al., 2015). Further, beyond single image data, PONO and MS may also be applied to video data (Wang et al., 2018; Li et al., 2018a), 3D voxel grids (Tran et al., 2015; Carreira and Zisserman, 2017), or tasks in natural language processing (Devlin et al., 2018).
This research is supported in part by the grants from Facebook, the National Science Foundation (III-1618134, III-1526012, IIS1149882, IIS-1724282, and TRIPODS-1740822), the Office of Naval Research DOD (N00014-17-1-2175), Bill and Melinda Gates Foundation. We are thankful for generous support by Zillow and SAP America Inc.
Appendix A Algorithm of PONO-MS
Appendix B Equations of Existing Normalization
Batch Normalization (BN) computes the mean and std across B, H, and H dimensions, i.e.
where is a small constant applied to handle numerical issues.
Synchronized Batch Normalization views features of mini-batches across multiple GPUs as a single mini-batch.
Instance Normalization (IN) treats each instance in a mini-batch independently and computes the statistics across only spatial dimensions, i.e.
Layer Normalization (LN) normalizes all features of an instance within a layer jointly, i.e.
Finally, Group Normalization (GN) lies between IN and LN, it devides the channels into groups and apply layer normalization within a group. When , GN becomes LN. Conversely, when the , it is identical to IN. To define it formally, it computes
Appendix C PONO Statistics of Models Pretrained on ImageNet
Appendix D Implementation details
Appendix E Qualitative Results Based on CycleGAN and Pix2pix
Appendix F Qualitative Results Based on DRIT and MUNIT.
We randomly sample 10 cat and dog image pairs and show the outputs of DRIT, DRIT + PONO-MS, MUNIT, and MUNIT’ PONO-MS in Figure 10.
Appendix G PONO in Image Classification
To evaluate PONO on image classification task, we add PONO to the begining of each ResBlock of ResNet-18 (He et al., 2016) (also affects the shortcut). We followed the common training procedure base on Wei Yang’s open sourced code
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