787 research outputs found
The Possibility of Transfer(?): A Comprehensive Approach to the International Criminal Tribunal for Rwanda’s Rule 11bis To Permit Transfer to Rwandan Domestic Courts
We present a learned image compression system based on GANs, operating at
extremely low bitrates. Our proposed framework combines an encoder,
decoder/generator and a multi-scale discriminator, which we train jointly for a
generative learned compression objective. The model synthesizes details it
cannot afford to store, obtaining visually pleasing results at bitrates where
previous methods fail and show strong artifacts. Furthermore, if a semantic
label map of the original image is available, our method can fully synthesize
unimportant regions in the decoded image such as streets and trees from the
label map, proportionally reducing the storage cost. A user study confirms that
for low bitrates, our approach is preferred to state-of-the-art methods, even
when they use more than double the bits.Comment: E. Agustsson, M. Tschannen, and F. Mentzer contributed equally to
this work. ICCV 2019 camera ready versio
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
Edge- and Node-Disjoint Paths in P Systems
In this paper, we continue our development of algorithms used for topological
network discovery. We present native P system versions of two fundamental
problems in graph theory: finding the maximum number of edge- and node-disjoint
paths between a source node and target node. We start from the standard
depth-first-search maximum flow algorithms, but our approach is totally
distributed, when initially no structural information is available and each P
system cell has to even learn its immediate neighbors. For the node-disjoint
version, our P system rules are designed to enforce node weight capacities (of
one), in addition to edge capacities (of one), which are not readily available
in the standard network flow algorithms.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
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