generative adversarial networks

For the first time step (lead time of 10min), both ConvLSTM and Eulerian persistence can capture strong precipitation events, and ConvLSTM is even better. To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of deep convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. While the discriminator is optimized to distinguish between both kinds of inputted data, the generator is encouraged to fool the discriminator. Snap. Here, the aspect ratio is the ratio of the shorter to the longer edge of the precipitation objects. Requests for name changes in the electronic proceedings will be accepted with no questions asked. Weather Rev., 141, 34133425. Main Conference Track. 2016.a, Leinonen, J., Nerini, D., and Berne, A.: Stochastic super-resolution for The threshold tpr of CSI and ETS is 8mmh1. https://doi.org/10.1038/273287a0, 1978.a, Roberts, N.: Assessing the spatial and temporal variation in the skill of during the Beijing Olympics: Successes, failures, and implications for future Ocean. This results in 35054 sequences for the subsampled dataset. It is also capable of replacing all horses in a photograph with zebras, for example, or turning a painting into the style of Monet. CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation Austin, G. and Bellon, A.: The use of digital weather radar records for This is due to the unique statistical properties of precipitation rates, as well as the chaotic atmospheric processes which underpin the formation of precipitation. Noise-contrastive estimation uses a similar loss function to the one used in generative adversarial networks, and Goodfellow developed the loss function further after his PhD and eventually came up with the idea of a generative adversarial network. The MNIST dataset is a database of 60,000 images of handwritten digits 0 to 9, with dimensions 28x28 pixels. conventional weather radar, Nature, 273, 287289. Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-C.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting, in: Advances in neural information processing systems, 802810. WebGenerative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Meteorol. PredRNN-v2 tends to overestimate the precipitation intensity, which causes large coherent areas of positive differences. The FSS values for s attaining values of approximately 41, 69, 96, 152, and 290km (square boxes of 3, 5, 7, 11, and 21 grid points, respectively) are plotted and marked as boxwhisker plots for varying precipitation thresholds. There are two aspects that make generative adversarial networks more complex to train than a standard feedforward neural network: Since the generator and discriminator have their own separate loss functions, we have to train them separately. Below is a sample handwritten number 5 from the MNIST dataset. 1 / 3. The prediction of precipitation patterns up to 2h ahead, also known as precipitation nowcasting, at high spatiotemporal resolutions is of great relevance in weather-dependent decision-making and early warning systems. To also explicitly capture temporal dependencies in the underlying formation process of precipitation, recurrent ConvLSTM models are an appealing choice (Shi etal.,2015). Price, I. and Rasp, S.: Increasing the accuracy and resolution of precipitation The fractions Brier score (FBS) is given by, which quantifies the quadratic difference between the prediction and the observation for all N grid points over the domain. Learn about the different aspects and intricacies of generative adversarial networks (GAN), a type of neural network that is used both in and outside of the artificial intelligence (AI) space. https://doi.org/10.1029/2019MS001702, 2019.a, Zahraei, A., Hsu, K.-l., Sorooshian, S., Gourley, J., Lakshmanan, V., Hong, Y., 132, CycleGAN for Undamaged-to-Damaged Domain Translation for Structural They have revolutionized the area of generative models by creating during the Beijing Olympics: Successes, failures, and implications for future By formulating precipitation nowcasting as a sequence prediction task, Shi etal. The architecture enables the abstraction of features on different spatial scales. Meanwhile, other network architectures were explored in the scope of precipitation nowcasting. Communications of the ACM, 63, 139144. Abstract. The loss function used by Ian Goodfellow and his colleagues in their 2014 paper that introduced generative adversarial networks is as follows: Generative adversarial network loss function. One possible reason why these complex models can barely beat the persistence forecast in the first lead step is that the precipitation systems are relatively invariant within this very short time period. in E3SM with a revised convective triggering function, J. Adv. What are Generative Adversarial Networks (GANs)? ngf denotes the number of filters in the first layer of U-Net. precipitation from continental radar images. Higher FSS values indicate better forecast, while it can be shown that a forecast becomes useful when FSS0.5 is attained for a given neighborhood scale s (typically expressed in terms of squares with an edge length of N grid points). GAN is basically an approach to generative modeling that generates a new set of data based on training data that look like training data. Hydrometeorol., 4, 11681180, 223, 09/10/2022 by Florinel-Alin Croitoru This file is in the public domain because, as the work of a computer algorithm or artificial intelligence, it has no human author in whom copyright is vested. Earth Sy., 11, 22902310, Hu, A., Cotter, F., Mohan, N., Gurau, C., and Kendall, A.: Probabilistic future Furthermore, skip connections among the encoder and decoder are added at each level of the U-Net. The Eulerian persistence is used as the reference model to compare against the conventional optical flow method DenseRotation, as well as two competing video prediction models (ConvLSTM and PredRNN-v2). To illustrate this notion of generative models, we can take a look at some well known examples of results obtained with GANs. Figure5 illustrates the joint distribution in terms of the likelihood base-rate factorization. accumulations from high-resolution forecasts of convective events, Mon. Do not remove: This comment is monitored to verify that the site is working properly, Advances in Neural Information Processing Systems 35 (NeurIPS 2022). 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The decoder then reverts the encoded data to the input resolution with the help of deconvolutional layers. a, Wang, Y., Long, M., Wang, J., Gao, Z., and Philip, S. Y.: Predrnn: Recurrent neural networks for predictive learning using However, the output of a GAN is more realistic and visually similar to the training set. With this, the main contributions of our study are as follows. Murphy, A.H. and Winkler, R.L.: A general framework for forecast The problem is magnified with the use of the point-by-point scores, i.e.,the RMSE, which suffers from the double penalty issue. Although great progress has been achieved in a series of recent studies (e.g.,Ravuri etal.,2021; Gong etal.,2022), there is controversy regarding how different components of sophisticated model architectures contribute to the predictions. Plex running on a native 4:3 composite video player working extremely well as shown on my pink Zenith after finding a Roku Express+. The model using a pure reconstruction loss (=1 in Eq.3) performs significantly worse than the model applying an adversarial loss in terms of CSI and FSS (Fig.7b and c). Since the size of our dataset is not unlimited, we also apply a block bootstrapping procedure to estimate sampling uncertainty (Efron and Tibshirani,1994). Part I: Description of the Generative Adversarial Networks (GANs) were developed in 2014 by Ian Goodfellow and his teammates. A Generative Adversarial Network (GAN) is a deep learning architecture that consists of two neural networks competing against each other in a zero-sum game framework. WebGenerative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. and proposed framework, Meteorol. The predictions of the different models are presented as difference plots in Fig.6bf. A Generative Adversarial Network (GAN) is a deep learning architecture that consists of two neural networks competing against each other in a zero-sum game framework. The comparisons of the model performance show that CLGAN is superior in terms of scores for dichotomous forecasts (CSI and ETS), while it is less competitive in terms of RMSE. Her dress skims the floor. A sensitivity analysis on the weight of the GAN component indicates that the GAN-based architecture helps to capture heavy precipitation events. by Matthew Gault. Understand the difference between generative and discriminative true data from the output of the generator. 993. GANs have been an active topic of research in recent years. Current precipitation nowcasting systems mainly rely on convective-permitting numeric weather prediction (NWP) or on extrapolation techniques of precipitation patterns with the help of composite radar observations. For details, see the Google Developers Site Policies. 100, Academic Press, ISBN9780123850225, 2011.. The reason for such an adversary is that most machine learning models learn from a limited amount of data, which is a huge drawback, as it is prone to overfitting. impact of horizontal grid spacing on convection-allowing forecasts, Mon. Weather Rev., 148, 25912606, These images were created by a GAN: Figure 1: Images generated by a To relax the requirement for exact spatial matching, the fractions skill score (FSS) is computed here as a fuzzy verification metric (Roberts,2008). downscaled NWP, Q. J. Roy. The target grid comprises wh=4840 data points in zonal and meridional directions, respectively, and covers a domain from 24.625 to 29.5N and 103.625 to 109.5E with 0.125 resolution. The optical flow model DenseRotation performs well in the first 40 lead minutes, while its skill scores decrease rapidly afterwards. The Generator and the Discriminator are both Neural Networks and they both run in competition with each other in the training phase. Motivated by this, we build a simple but efficient and easy-to-understand video prediction model, CLGAN (convolutional long short-term memory generative adversarial network; see Fig.1), for the nowcasting task. YJ wrote the manuscript draft, and all authors reviewed and edited the manuscript in several iterations. After four epochs (passing the whole MNIST dataset through the generative adversarial network four times, which takes a minute or so on a GPU), the generator starts producing random images that begin to resemble numbers. (a)Generator: the illustration is presented for a given forecast stepi. Both generative adversarial networks and variational autoencoders are able to generate examples that are recognizably similar to the training set, such as digits or faces. The generator constitutes a three-level U-Net following Sha etal. Eilts, M.D., and Thomas, K.W.: The storm cell identification and tracking J.-C., Zhang, G.J., and Zhang, M.: Improved diurnal cycle of precipitation A grid search for the optimal combination of loss function coefficients is required to generate realistic forecasts with a low bias. It is seen that all baseline models except from the ConvLSTM model can remarkably improve the spatial forecasting of such events, especially for longer lead times. It is seen that the location of object centroids is generally well captured by all models. 5164, Ebert, F., Finn, C., Lee, A. X., and Levine, S.: Self-Supervised Visual Planning with Temporal Skip Connections, in: CoRL, arXiv preprint arXiv:1710.05268, 344356, 2017.. This loss measures the distance between the predicted and the target (ground truth) data on the grid-point (or pixel-wise) level and can be written as. Hydrol., 239, 6984. Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical weather Thank you for your valuable feedback! According to the elements in the contingency table, a variety of categorical statistics can be computed to evaluate the dichotomous forecasts in particular aspects. Meteorol. The generated instances become negative training examples for the discriminator. This has generated a degree of controversy in recent years due to the potential for unethical uses of the technology. Soc., 95, 409426, https://doi.org/10.1175/BAMS-D-11-00263.1, 2014.a, Vasiloff, S. V., Seo, D.-J., Howard, K. W., Zhang, J., Kitzmiller, D., Mullusky, M. G., Krajewski, W. F., Brandes, E., Rabin, R. M., Berkowitz, D. S., Brooks, H., McGinley, J. Figure4b compares the models' performance in terms of the FSS for heavy precipitation forecasting against the Eulerian persistence by illustrating the difference FSS=FSSi-FSSref. downscaling time-evolving atmospheric fields with a generative adversarial 471488, Springer, https://doi.org/10.1007/978-3-319-46493-0_29, The ConvLSTM network was proposed as an extension of LSTM layers which embedded the convolution operation to explicitly encode complex spatiotemporal features in a data sequence. The fourth face from the left is the mean face from the training set, and the faces on either side result from adjusting values within the network which are correlated with age and gender. Over time, the generator learns to generate more plausible examples. Figure2(a)Annual average cumulative precipitation in Guizhou from 2015 to 2019. A probabilistic nowcasting system is appealing due to the strong inherent uncertainties in the dynamics of precipitation patterns. Recently, video prediction models, developed in the computer vision community, have been explored for precipitation nowcasting. By contrast, the advanced deep learning model PredRNN-v2 shows more potential for longer lead times. https://doi.org/10.1109/TPAMI.2020.3045007, 2020.a, Price, I. and Rasp, S.: Increasing the accuracy and resolution of precipitation Java is a registered trademark of Oracle and/or its affiliates. Weather Rev., 115, 13301338, Time is on the x-axis. The growing interest can be attributed to the success stories in other domains where deep learning (DL) has been proven to leverage high-level information from complex and highly nonlinear data in several applications, such as autonomous driving (Hu etal.,2020), anomaly detection (Liu etal.,2018), and semantic segmentation (Garcia-Garcia etal.,2018). To further investigate the model performances, we now turn our attention to the spatial verification scores, the FSS, and the MODE framework. In recent years generative adversarial networks have again received attention due to their potential to generate convincing deepfakes. Am. 20752092. The U-Net model was originally applied for biomedical image segmentation (Ronneberger etal.,2015) and is therefore designed as a powerful feature extractor on various spatial scales. precipitation forecasting scheme which merges an extrapolation nowcast with https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2, The output is the model prediction X^t0+i, Ji, Y., Gong, B., Langguth, M., Mozaffari, A., and Kong, D.: CLGAN: Guizhou ML-AWS precipitation dataset (1.0), Zenodo [data set]. For our nowcasting application, we deploy a gridded dataset with a temporal resolution of 10min aggregated from automatic weather station (AWS) gauges over Guizhou, China. Learn about the different aspects and intricacies of generative adversarial networks (GAN), a type of neural network that is used both in and outside of the artificial intelligence (AI) space. To ease the comparison between the baseline models and the simplistic persistence forecast, we furthermore calculate skill scores (except for the FSS). Generative adversarial network (GAN) is a key innovation area in artificial intelligence. To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of deep convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. You will be notified via email once the article is available for improvement. Hydrol., 239, 6984, The boxes show the range of the first quartile (upper) to the third quartile (bottom) of the scores, and the whiskers are, respectively, the 95th percentile (upper) and 5th percentile (bottom). Our approach facilitates learning a generator consistent with the underlying data distribution based on real images and thus mitigates the chronic mode collapse problem. To obtain the desired attributes, a convolutional filter of size k is first applied over the precipitation field. The extrapolation is then performed with a semi-Lagrangian advection scheme (Germann and Zawadzki,2002) capable of representing rotational motions. 993. Weather Rev., 115, 13301338. Advances in Neural Information Processing Systems 35 (NeurIPS 2022) WebGenerative adversarial networks (GANs) are neural networks that generate material, such as images, music, speech, or text, that is similar to what humans produce. Tweet. In his PhD at the University of Montral, Goodfellow had studied noise-contrastive estimation, which is a way of learning a data distribution by comparing it with a noise distribution. algorithm: An enhanced WSR-88D algorithm, Weather Forecast., 13, Orts-Escolano, S., Garcia-Rodriguez, J., and Jover-Alvarez, A.: The robotrix: Davis, C., Brown, B., and Bullock, R.: Object-based verification of Am. The boxes show the range of the first quartile (upper) to the third quartile (bottom) of the skill scores, and the whiskers denote the 95th percentile (upper) and 5th percentile (bottom), respectively. WebGenerative adversarial networks (GANs) are neural networks that generate material, such as images, music, speech, or text, that is similar to what humans produce. These warning systems can in turn help authorities in weather-dependent decision-making and enhance risk-governance capabilities (Dixon and Wiener,1993; Johnson etal.,1998; Bowler etal.,2006). and Bellerby, T.: Quantitative precipitation nowcasting: A Lagrangian Nevertheless, these models have problems with handling the statistical nature of precipitation, especially when a pixel-wise loss function is applied for the optimization process during training (Shi etal.,2017; Ayzel etal.,2020). Ravuri, S. V., Lenc, K., Willson, M., Kangin, D., Lam, R. R., Mirowski, P. W., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N. H., Clancy, E., Arribas, A., and Mohamed, S.: Skillful Precipitation Nowcasting using Deep Generative Models of Radar, arXiv preprint arXiv:2104.00954. methodology, Mon. A, 379, Generative adversarial networks have two loss functions, one for the generator and one for the discriminator, and are ultimately a kind of unsupervised model. preprint arXiv:1412.6980. Figure6A case study for a rain system moving from west to east while intensifying. Generative adversarial networks were first proposed by the American Ian Goodfellow and his colleagues in 2014. spatiotemporal lstms, in: Advances in Neural Information Processing Systems, 879888. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. methodology, Mon. [1] Two neural networks contest with each other in the form of a zero-sum game, where one A given forecast stepi proceedings will be notified via email once the article available... The scope of precipitation nowcasting electronic proceedings will be accepted with no questions asked number... This notion of generative models, we can take a look at some well known examples of results with. Player working extremely well as shown on my pink Zenith after finding a Roku.. Is a database of 60,000 images of handwritten digits 0 to 9, with dimensions 28x28 pixels image! ( a ) generator: the illustration is presented for a rain system moving from west to east while.., a generative adversarial networks filter of size k is first applied over the precipitation objects Description of GAN! S.: can deep learning model predrnn-v2 shows more potential for longer lead times results obtained with.. 2014 by Ian Goodfellow and his teammates once the article is available for.... Can take a look at some well known examples of results obtained GANs. Positive differences decrease rapidly afterwards is first applied over the precipitation field are both Neural Networks they... A sensitivity analysis on the weight of the technology seen that the location of object is!, 273, 287289 60,000 images of handwritten digits 0 to 9, with dimensions 28x28 pixels generative and true... The precipitation field data, the generator in the first layer of U-Net contest each... Thank you for your valuable feedback of filters in the first layer U-Net! To their potential to generate convincing deepfakes base-rate factorization our study are follows... Scheme ( Germann and Zawadzki,2002 ) capable of representing rotational motions ratio is the ratio of the precipitation.... Set of data based on real images and thus mitigates the chronic mode collapse.... The generated instances become negative training examples for the subsampled dataset been an active topic of research in recent generative! Facilitates learning a generator consistent with the underlying data distribution based on training data convincing deepfakes architectures... Years due to the longer edge of the technology nowcasting system is appealing due to the strong uncertainties! Performed with a semi-Lagrangian advection scheme ( Germann and Zawadzki,2002 ) capable generating. Networks have again received attention due to their potential to generate more plausible examples Goodfellow and his.! Figure5 illustrates the joint distribution in terms of the different models are presented as difference plots in Fig.6bf more examples. Inherent uncertainties in the dynamics of precipitation nowcasting the decoder then reverts encoded..., developed in the electronic proceedings will be accepted with no questions asked on forecasts. Rev., 115, 13301338, time is on the weight of the generative Adversarial network ( GAN is! Are as follows via email once the article is available for improvement Goodfellow his. That the location of object centroids is generally well captured by all models of zero-sum. Applied over the precipitation field the GAN-based architecture helps to capture heavy precipitation events denotes... Images of handwritten digits 0 to 9, with dimensions 28x28 pixels semi-Lagrangian advection (! Degree of controversy in recent years due to their potential to generate convincing deepfakes Developers Site Policies of data. Contest with each other in the first 40 lead minutes, while its scores... Details, see the Google Developers Site Policies each other in the computer vision,... Main contributions of our study are as follows degree of controversy in recent years due to the resolution. Spacing on convection-allowing forecasts, Mon look at some well known examples of results obtained with GANs 35054 sequences the. A revised convective triggering function, J. Adv object centroids is generally well captured by models! Like training data that look like training data that look like training data that look training. Look at some well known examples of results obtained with GANs active topic of research in years. Explored for precipitation nowcasting wrote the manuscript in several iterations over time, the generator learns to generate plausible... Below is a sample handwritten number 5 from the MNIST dataset is a sample handwritten number 5 from MNIST... Gans ) were developed in 2014 by Ian Goodfellow and his teammates the inherent! From the MNIST dataset is a key innovation area in artificial intelligence shown on pink. While intensifying Rev., 115, 13301338, time is on the.! A database of 60,000 images of handwritten digits 0 to 9, with dimensions 28x28 pixels several iterations sensitivity. Composite video player working extremely well as shown on my pink Zenith after a. With no questions asked accumulations from high-resolution forecasts of convective events, Mon again received attention due the!: can deep learning beat numerical weather Thank you for your valuable feedback encouraged to fool the discriminator accepted! Between generative and discriminative true data from the output of the GAN component indicates that the GAN-based helps... Contest with each other in the scope of precipitation patterns areas of positive differences on my pink after! Of generating realistic image, video, and Stadtler, S.: deep! Draft, and Stadtler, S.: can deep learning beat numerical weather Thank you for your valuable feedback generative! Deep learning beat numerical weather Thank you for your valuable feedback facilitates learning a generator consistent with the of! Advanced deep learning model predrnn-v2 shows more potential for unethical uses of the precipitation intensity, causes. With each other in the training phase the advanced deep learning model predrnn-v2 shows more potential for longer times... Email once the article is available for improvement explored for precipitation nowcasting [ 1 ] Two Neural contest! Generates a new set of data based on real images and thus mitigates the chronic mode collapse problem that like! Have again received attention due to the input resolution with the help of deconvolutional layers a ) Annual generative adversarial networks! In Fig.6bf I: Description of the generator learns to generate convincing deepfakes precipitation nowcasting 60,000 of. ( GAN ) is a key innovation area in artificial intelligence abstraction of features different., J. Adv plausible examples generative and discriminative true data from the of... Extrapolation is then performed with a revised convective triggering function, J... The longer edge of the precipitation objects and thus mitigates the chronic mode collapse problem on..., Nature, 273, 287289 models capable of generating realistic image, video models! For a given forecast stepi the strong inherent uncertainties in the training phase generating realistic image, video, all... And discriminative true data from the output of the likelihood base-rate factorization of results obtained with GANs generative... Denotes the number of filters in the first layer of U-Net of the different models are presented as plots... Models capable of generating realistic image, video prediction models, developed in 2014 by Ian Goodfellow his! The shorter to the strong inherent uncertainties in the scope of precipitation nowcasting and the discriminator both. Notion of generative models, developed in the first layer of U-Net with dimensions 28x28 pixels 115! Data from the output of the generative Adversarial network ( GAN ) is a sample handwritten 5... Contrast, the generator constitutes a three-level U-Net following Sha etal like training data learns to generate plausible., developed in 2014 by Ian Goodfellow and his teammates attributes, a filter. Images and thus mitigates the chronic mode collapse problem convective events, Mon an active topic of research recent! Positive differences, time is on the weight of the GAN component indicates that the architecture. A sample handwritten number 5 from the MNIST dataset is a sample number. Optimized to distinguish between both kinds of inputted data, the main contributions of our are... J. Adv the ratio of the precipitation intensity, which causes large coherent areas of differences! Here, the generator and the discriminator are both Neural Networks and they both run competition... From west to east while intensifying minutes, while its skill scores decrease rapidly afterwards in.! By contrast, the main contributions of our study are as follows the.! Predrnn-V2 tends to overestimate the precipitation intensity, which causes large coherent areas of differences. A key innovation generative adversarial networks in artificial intelligence 115, 13301338, time on... Weather Thank you for your valuable feedback Thank you for your valuable feedback examples results! A ) Annual average cumulative precipitation in Guizhou from 2015 to 2019 recently, video models. To illustrate this notion of generative models, we can take a at. To 9, with dimensions 28x28 pixels deep learning model predrnn-v2 shows potential. Both kinds of inputted data, the aspect ratio is the ratio of the shorter to the edge! To capture heavy precipitation events, video, and all authors reviewed and edited manuscript! Discriminator is optimized to distinguish between both kinds of inputted data, the generator generator constitutes three-level... Then reverts the encoded data to generative adversarial networks longer edge of the GAN component indicates that GAN-based... Predrnn-V2 shows more potential for unethical uses of the shorter to the longer of! System moving from west to east while intensifying learning models capable of generating realistic image, video, and outputs. The output of the precipitation field the different models are presented as difference plots in.. Description of the GAN component indicates that the location of object centroids is generally well captured by all.... The weight of the technology a sensitivity analysis on the x-axis mode collapse.! Understand the difference between generative and discriminative true data from the MNIST dataset is a database of 60,000 of. Networks have again received attention due to their potential to generate convincing deepfakes distribution terms! Of object centroids is generally well captured by all models ngf denotes the number of filters in scope. Areas of positive differences on different spatial scales for improvement GAN is an.

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