# Tag Info

Accepted

### What is the meaning of $V(D,G)$ in the GAN objective function?

To understand this equation first you need to understand the context in which it is first introduced. We have two neural networks (i.e. $D$ and $G$) that are playing a minimax game. This means that ...
• 3,143
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### Is plain autoencoder a generative model?

An autoencoder is not considered a generative model, because it only reconstructs the given input. You could use the decoder like a generative model by putting in different vectors. However, the ...
• 1,327
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### How can we process the data from both the true distribution and the generator?

The Focus of This Question "How can ... we process the data from the true distribution and the data from the generative model in the same iteration? Analyzing the Foundational Publication In the ...
• 7,375

### Why diffusion model always use U-Net?

I don't have a definitive answer but I'd state my intuitions anyways: Diffusion models are highly related to the idea of stacked denoising autoencoders [Kumar et al. (2014)]. Additionally, U-Net-like ...
• 1,327

### Why is the variational auto-encoder's output blurred, while GANs output is crisp and has sharp edges?

The key is: VAE usually use a small latent dimension, the information of input is so hard to pass through this bottleneck, meanwhile it tries to minimize the loss with the batch of input data, you ...
• 49
Accepted

### Other deep learning image generation techniques besides GANs?

There are several generative models that have been proposed before or roughly at the same time of the GAN (2014). For example, the deep Boltzman machine (2009), deep generative stochastic network (...
• 37k
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• 9,037
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### How can AI be used to more reliably analyze and plan around the tie between climate and emissions?

Can AI provide a more reliable analysis of the gross effects of carbon emissions on extinctions of species ice-cap melting, and other effects? Yes. The work of Judea Pearl and others over the last 20 ...
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• 3,143

### What parameters can be tweaked to avoid a generator or discriminator loss collapsing to zero when training a DC-GAN?

GANs are notably hard to train and it is not uncommon to have large bumps in the losses. The learning rate is a good start but the instability may come from a wide variety of reasons. I'm assuming ...
• 256
Accepted

### Does MMD-VAE solve the problem of blurred images of vanilla VAEs?

[Answering my own question after 5 months of studying VAE models] The point of the MMD-VAE or InfoVAE is not exactly to emphasise on the visual quality of generated samples. It is to preserve greater ...
• 148