All Questions
Tagged with variational-autoencoder convolutional-neural-networks
9 questions
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How to solve the exploding gradient problem in VAE training?
I was trying to implement VAE on the CelebA dataset inspired by the Tensorflow implementation of MNIST. I have tried varying batch size but there seems to be no effect from that. The image formed is ...
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1
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How to remove random noise from an image (denoising)?
When adding noise to an image, for instance,
is the noise added evenly random (equally likely values within some range), or random but following some distribution (like the normal distribution)?
Then,...
- 157
3
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1
answer
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How to perform latent space Interpolation between two images?
I have a variational convolutional autoencoder that has trained on 2 images and outputs a linear interpolation (inserted at the bottleneck stage) between those 2 input images.
However, the result ...
- 157
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VAE for Motion Sequence Generation - Convergence Issue with Scheduled Sampling
I have implemented a Variational Autoencoder (VAE) in PyTorch for motion sequence generation using human pose data (joint angles and angular velocities in radians) from the CMU dataset. The VAE ...
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287
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How to colorize images with Variational Autoencoder?
CONTEXT
I'm trying to colorize images with Variational Autoencoder. Input is 256x256 gray image. Output is 256x256x2 as I convert image to a LAB color space and then put gray channel as input and ...
- 242
3
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How do you calculate KL divergence on a three-dimensional space for a Variational Autoencoder?
I'm trying to implement a variational auto-encoder (as seen in Section 3.1 here: https://arxiv.org/pdf/2004.06271.pdf).
It differs from a traditional VAE because it encodes its input images to three-...
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2
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277
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How to construct input dependent convolutional filter?
I am constructing a convolutional variational autoencoder for images, starting out with mnist digits. Typically I would specify convolutional layers in the following way:
...
- 143
3
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What makes GAN or VAE better at image generation than NN that directly maps inputs to images
Say a simple neural network's input is a collection of tags (encoded in some way), and the output is an image that corresponds to those tags. Say this network consists of some dense layers and some ...
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Concrete example of latent variables and observables plugged into the Bayes' rule
In the context of the variational auto-encoder, can someone give me a concrete example of the application of the Bayes' rule
$$p_{\theta}(z|x)=\frac{p_{\theta}(x|z)p(z)}{p(x)}$$
for a given latent ...
- 805