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  1. Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge.[1]
  2. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset.[1]
  3. Let us take the example of training a generative adversarial network to synthesize handwritten digits.[2]
  4. However, the output of a GAN is more realistic and visually similar to the training set.[2]
  5. Three synthetic faces generated by the generative adversarial network StyleGAN, developed by NVIDIA.[2]
  6. In 2018, a group of three Parisian artists called Obvious used a generative adversarial network to generate a painting on canvas called Edmond de Belamy.[2]
  7. Two of the most popular generative models in chemistry are the variational autoencoder (VAE) (38) and generative adversarial networks (GAN).[3]
  8. On the other hand, a GAN uses a decoder (or generator) and discriminator to learn the materials data distribution implicitly.[3]
  9. We will further describe the framework in the Composition-Conditioned Crystal GAN section.[3]
  10. Variational autoencoders are capable of both compressing data like an autoencoder and synthesizing data like a GAN.[4]
  11. Each side of the GAN can overpower the other.[4]
  12. On a single GPU a GAN might take hours, and on a single CPU more than a day.[4]
  13. This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN).[5]
  14. This tutorial has shown the complete code necessary to write and train a GAN.[5]
  15. Taken one step further, the GAN models can be conditioned on an example from the domain, such as an image.[6]
  16. Develop Your GAN Models in Minutes ...with just a few lines of python code ...[6]
  17. and much more... Finally Bring GAN Models to your Vision Projects Skip the Academics.[6]
  18. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics.[7]
  19. For example, a GAN trained on the MNIST dataset containing many samples of each digit, might nevertheless timidly omit a subset of the digits from its output.[7]
  20. To further leverage the symmetry of them, an auxiliary GAN is introduced and adopts generator and discriminator models of original one as its own discriminator and generator respectively.[7]
  21. Each GAN model was trained 10,000 epochs and once the training was finished, 50,000 compounds were sampled from the generator and decoded with the heteroencoder.[8]
  22. Our generative ML model for inorganic materials (MatGAN) is based on the GAN scheme as shown in Fig.[9]
  23. We found the integer representation of materials greatly facilities the GAN training.[9]
  24. In our GAN model, both the discriminator (D) and the generator (G) are modeled as a deep neural network.[9]
  25. During our GAN generation experiments for OQMD dataset, we found that it sometimes has difficulty to generate a specific category of materials.[9]
  26. The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator.[10]
  27. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance.[10]
  28. Adversarial Networks (GAN) was introduced into the field of deep learning by Goodfellow et al.[10]
  29. As can be seen from its name, GAN, a form of generative models, is trained in an adversarial setting deep neural network.[10]
  30. The big insights that defines a GAN is to set up this modeling problem as a kind of contest.[11]
  31. Instead, we're showing a GAN that learns a distribution of points in just two dimensions.[11]
  32. At top, you can choose a probability distribution for GAN to learn, which we visualize as a set of data samples.[11]
  33. To start training the GAN model, click the play button ( ) on the toolbar.[11]
  34. GAN, that is conceptually simple, stable at training and resistant to mode collapse.[12]
  35. This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models.[13]
  36. In the third section, we introduce some new derivative models on loss function and model structure in comparison with the traditional GAN models, along with analyzing the hidden space of GANs.[13]
  37. GAN is a generative model that generates target data by latent variables.[13]
  38. The emergence of WCGAN has brought the GAN models to a new height.[13]
  39. One dog is real, one is generated by the DC-GAN algorithm.[14]
  40. The Wasserstein GAN (W-GAN) marked a recent and major milestone in GAN development, developed by Martin Arjovsky at NYU’s Courant Institute of Mathematical Sciences together with Facebook researchers.[14]
  41. The W-GAN has two big advantages: It is easier to train than a standard GAN because the cost function provides a more robust gradient signal.[14]
  42. To prove the point, we took our W-GAN implementation and trained it on the LSUN bedroom dataset both in 32-bit floating point and Flexpoint with a 16-bit mantissa and 5-bit exponent.[14]
  43. One clever approach around this problem is to follow the Generative Adversarial Network (GAN) approach.[15]
  44. In this work, Tim Salimans, Ian Goodfellow, Wojciech Zaremba and colleagues have introduced a few new techniques for making GAN training more stable.[15]
  45. Peter Chen and colleagues introduce InfoGAN — an extension of GAN that learns disentangled and interpretable representations for images.[15]
  46. The image at the top represents the output of a GAN without mode collapse.[16]
  47. The image at the bottom represents the output of a GAN with mode collapse.[16]
  48. A common question in GAN training is “when do we stop training them?”.[16]
  49. The GAN objective function explains how well the Generator or the Discriminator is performing with respect to its opponent.[16]
  50. We’ll explore the GAN framework along with its components -- generator and discriminator networks.[17]
  51. We investigated the output data trends and network parameters of the GAN generator to identify how the network extracts biological features.[18]
  52. In June 2019, Microsoft researchers detailed ObjGAN, a novel GAN that could understand captions, sketch layouts, and refine the details based on the wording.[19]
  53. Startup Vue.ai‘s GAN susses out clothing characteristics and learns to produce realistic poses, skin colors, and other features.[19]
  54. Scientists at Carnegie Mellon last year demoed Recycle-GAN, a data-driven approach for transferring the content of one video or photo to another.[19]
  55. Their proposed system — GAN-TTS — consists of a neural network that learned to produce raw audio by training on a corpus of speech with 567 pieces of encoded phonetic, duration, and pitch data.[19]
  56. A GAN is a type of neural network that is able to generate new data from scratch.[20]
  57. In my experiments, I tried to use this dataset to see if I can get a GAN to create data realistic enough to help us detect fraudulent cases.[20]
  58. You can hear the inventor of GANs, Ian Goodfellow, talk about how an argument at a bar on this topic led to a feverish night of coding that resulted in the first GAN.[20]
  59. The examples in GAN-Sandbox are set up for image processing.[20]
  60. In the GAN-based design, the discriminative network will map out the relationship between configurations and properties through learning the provided dataset.[21]
  61. Examples of GAN-generated architectured materials with E ∼ mean ( Ω ≤ 5 % ) achieving more than 94% of E HS .[21]
  62. GAN is a recently developed machine learning framework proposed to creatively generate complex outputs, such as fake faces, speeches, and videos (44).[21]
  63. We train a GAN for each symmetry group separately.[21]

소스[편집]

  1. 1.0 1.1 Training Generative Adversarial Networks with Limited Data
  2. 2.0 2.1 2.2 2.3 Generative Adversarial Network
  3. 3.0 3.1 3.2 Generative Adversarial Networks for Crystal Structure Prediction
  4. 4.0 4.1 4.2 A Beginner's Guide to Generative Adversarial Networks (GANs)
  5. 5.0 5.1 Deep Convolutional Generative Adversarial Network
  6. 6.0 6.1 6.2 A Gentle Introduction to Generative Adversarial Networks (GANs)
  7. 7.0 7.1 7.2 Generative adversarial network
  8. A de novo molecular generation method using latent vector based generative adversarial network
  9. 9.0 9.1 9.2 9.3 Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
  10. 10.0 10.1 10.2 10.3 Generative Adversarial Networks and Its Applications in Biomedical Informatics
  11. 11.0 11.1 11.2 11.3 GAN Lab: Play with Generative Adversarial Networks in Your Browser!
  12. Chi-square Generative Adversarial Network
  13. 13.0 13.1 13.2 13.3 Generative Adversarial Network Technologies and Applications in Computer Vision
  14. 14.0 14.1 14.2 14.3 Training Generative Adversarial Networks in Flexpoint
  15. 15.0 15.1 15.2 Generative Models
  16. 16.0 16.1 16.2 16.3 Advances in Generative Adversarial Networks (GANs)
  17. Introduction to Generative Adversarial Networks (GAN) with Apache MXNet
  18. A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease
  19. 19.0 19.1 19.2 19.3 Generative adversarial networks: What GANs are and how they’ve evolved
  20. 20.0 20.1 20.2 20.3 Create Data from Random Noise with Generative Adversarial Networks
  21. 21.0 21.1 21.2 21.3 Designing complex architectured materials with generative adversarial networks

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