vq-vae-2-pytorch
benchmark_VAE
| vq-vae-2-pytorch | benchmark_VAE | |
|---|---|---|
| 2 | 4 | |
| 1,518 | 1,695 | |
| - | - | |
| 0.0 | 6.1 | |
| over 1 year ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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vq-vae-2-pytorch
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[D] GCP compute enging pricing question
not sure exactly what you mean by dimensionality of the forward passes, are you meaning the size of each layer? If it helps I've forked this https://github.com/rosinality/vq-vae-2-pytorch/blob/master/vqvae.py dimensionality of a single sample is [80000,3,1] ( batch size which works for me is about 8 size of the dataset is around 35000
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Using VQ-VAE to encode a matrix to a vector and back again
I am trying to use https://github.com/rosinality/vq-vae-2-pytorch for this purpose.
benchmark_VAE
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Researchers From INRIA France Propose ‘Pythae’: An Open-Source Python Library Unifying Common And State-of-the-Art Generative AutoEncoder (GAE) Implementations
Continue reading | Checkout the paper, github
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[P] Pythae - Unifying generative autoencoder implementations in Python
Code for https://arxiv.org/abs/2206.08309 found: https://github.com/clementchadebec/benchmark_VAE
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[P] Python library for Variational Autoencoder benchmarking
Github link: https://github.com/clementchadebec/benchmark_VAE
- Python library for Variational Autoencoder Benchmarking
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memorization - Code for "On Memorization in Probabilistic Deep Generative Models"