We propose a novel algorithm for quantizing continuous latent representations in trained models. Our approach applies to deep probabilistic models, such as variational autoencoders (VAEs), and enables both data and model compression. Unlike current end-to-end neural compression methods that cater the model to a fixed quantization scheme, our algorithm separates model design and training from quantization. Consequently,
our algorithm enables “plug-and-play” compression with variable rate-distortion trade-off, using a single trained model. Our algorithm can be seen as a novel extension of arithmetic coding to the continuous domain, and uses adaptive quantization accuracy based on estimates of posterior uncertainty. Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates using only a single
standard VAE. Further experiments on Bayesian neural word embeddings demonstrate the versatility of the proposed method.
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Variational Bayesian Quantization
We propose a novel algorithm for quantizing continuous
latent representations in trained models.
Our approach applies to deep probabilistic models,
such as variational autoencoders (VAEs), and
enables both data and model compression. Unlike
current end-to-end neural compression methods
that cater the model to a fixed quantization
scheme, our algorithm separates model design and
training from quantization. Consequently, our algorithm
enables “plug-and-play” compression at
variable rate-distortion trade-off, using a single
trained model. Our algorithm can be seen as a
novel extension of arithmetic coding to the continuous
domain, and uses adaptive quantization
accuracy based on estimates of posterior uncertainty.
Our experimental results demonstrate the
importance of taking into account posterior uncertainties,
and show that image compression with
the proposed algorithm outperforms JPEG over
a wide range of bit rates using only a single standard
VAE. Further experiments on Bayesian neural
word embeddings demonstrate the versatility
of the proposed method.
more »
« less
- Award ID(s):
- 1928718
- NSF-PAR ID:
- 10194432
- Date Published:
- Journal Name:
- Proceedings of the 37 th International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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