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Questions tagged [generative-model]

Machine-learning generative models, such as the neural network architectures Restricted Boltzmann Machine, Variational Autoencoder, Generative Adversarial Network.

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Adding voices to voice synthesis corpuses

If one uses one of the open source implementations of the WaveNet generative speech synthesis design, such as https://r9y9.github.io/wavenet_vocoder/, and trains using something like the CMU's arctic ...
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How can AI be used to more reliably analyze and plan around the tie between climate and emissions?

Note to the Duplicate Police This question is not a duplicate of the Q&A thread referenced in the close request. The only text even remotely related in that other thread is the brief mention of ...
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Why do we need Upsampling and Downsampling in Progressive Growing of Gans

I was working recently on Progressive Growing of GANs (aka PGGANs). I have implemented the whole architecture, but the problem that was ticking my mind is that in simple GANs, like DCGAN, PIX2PIX, we ...
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Deep learning with sparse input features

I training a generative adversarial network (GAN) to generate images given edge histogram descriptor (EHD) features of the image. The EHD features are themselves sparse (meaning they contain a lot of ...
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How do GAN's generator actually work?

I have implemented DCGAN's myself and have been studying GAN's for over a month now. Now I am implementing the pggans but I encountered a sentence When we measure the distance between the ...
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Can some one help me understand this paragraph from Nvidia's progressive gan paper?

Furthermore, we observe that mode collapses traditionally plaguing GANs tend to happen very quickly, over the course of a dozen minibatches. Commonly they start when the discriminator overshoots, ...
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What model is more efficient when modelling small sequences with small discrete inputs?

I Have a data where each example is having ten to twenty-time steps with small discrete input features about 50, my goal is to model P(X) as just one latent vector, i.e a hidden factor that could ...
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Performance Evaluation Metrics used in Training, Validation and Testing

Which specific performance evaluation metrics are used in training, validation and testing and why? I am thinking error metrics (RMSE, MAE, MSE) are used in validation, and testing should use a wide ...
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Coding CGAN paper model in Keras

I was coding a CGAN model using Keras along with the paper (https://arxiv.org/pdf/1411.1784.pdf) and I wanted to try and match the models to exactly what the paper says. I knew the models presented in ...
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Computing resources needed for Reinforcement Learning/Machine Imagery

For a school project I am wanting to investigate a paper on either Reinforcement Learning or Machine Imagery. Specifically, DQN's for Reinforcement Learning and RNNs, CNNs or LSTMs for Computer Vision/...
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Looking to build, compile, and/or find dataset for serial-parallelized code examples

I'm looking to perform two tasks: Train a classifier to classify code as serial or parallel Train a generative algorithm to generate parallel code from serial For the first task a simple scraper ...
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Neural network to get input attributes using only the output value

I have an idea about how to use neural networks but I'm not sure if it is possible or not. In supervised learning we have a set of attributes labeled with an output value. I can use these set to ...
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How to evaluate the goodness of images generated by GANs?

As we all know, there has been tons of GAN variants featuring different aspects of the image generation task such as stability, resolution or the ability to manipulate images. However, it is still ...