1,346 research outputs found
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
Practical Full Resolution Learned Lossless Image Compression
We propose the first practical learned lossless image compression system,
L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and
JPEG 2000. At the core of our method is a fully parallelizable hierarchical
probabilistic model for adaptive entropy coding which is optimized end-to-end
for the compression task. In contrast to recent autoregressive discrete
probabilistic models such as PixelCNN, our method i) models the image
distribution jointly with learned auxiliary representations instead of
exclusively modeling the image distribution in RGB space, and ii) only requires
three forward-passes to predict all pixel probabilities instead of one for each
pixel. As a result, L3C obtains over two orders of magnitude speedups when
sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN).
Furthermore, we find that learning the auxiliary representation is crucial and
outperforms predefined auxiliary representations such as an RGB pyramid
significantly.Comment: Updated preprocessing and Table 1, see A.1 in supplementary. Code and
models: https://github.com/fab-jul/L3C-PyTorc
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
The large memory requirements of deep neural networks limit their deployment
and adoption on many devices. Model compression methods effectively reduce the
memory requirements of these models, usually through applying transformations
such as weight pruning or quantization. In this paper, we present a novel
scheme for lossy weight encoding which complements conventional compression
techniques. The encoding is based on the Bloomier filter, a probabilistic data
structure that can save space at the cost of introducing random errors.
Leveraging the ability of neural networks to tolerate these imperfections and
by re-training around the errors, the proposed technique, Weightless, can
compress DNN weights by up to 496x with the same model accuracy. This results
in up to a 1.51x improvement over the state-of-the-art
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