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In the code for non-convolutional VQ-VAE, you seem to use BCE loss as the reconstruction loss for images. If I understand correctly, that would correspond to assuming that the image pixels follow a Bernoulli distribution, instead of the regular Gaussian assumption underlying MSE loss. Is this a deliberate choice? In the convolutional VQ-VAE operating on the same MNIST dataset, you use MSE.
The relevant line: https://github.com/nadavbh12/VQ-VAE/blob/a360e77d43ec43dd5a989f057cbf8e0843bb9b1f/vq_vae/auto_encoder.py#LL158C50-L158C50
The text was updated successfully, but these errors were encountered:
In the code for non-convolutional VQ-VAE, you seem to use BCE loss as the reconstruction loss for images. If I understand correctly, that would correspond to assuming that the image pixels follow a Bernoulli distribution, instead of the regular Gaussian assumption underlying MSE loss. Is this a deliberate choice? In the convolutional VQ-VAE operating on the same MNIST dataset, you use MSE.
The relevant line: https://github.com/nadavbh12/VQ-VAE/blob/a360e77d43ec43dd5a989f057cbf8e0843bb9b1f/vq_vae/auto_encoder.py#LL158C50-L158C50
The text was updated successfully, but these errors were encountered: