Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: COIN++: Data Agnostic Neural Compression   Neural compression algorithms are typically based on autoencoders that
require specialized encoder and decoder architectures for different data
modalities. In this paper, we propose COIN++, a neural compression framework
that seamlessly handles a wide range of data modalities. Our approach is based
on converting data to implicit neural representations, i.e. neural functions
that map coordinates (such as pixel locations) to features (such as RGB
values). Then, instead of storing the weights of the implicit neural
representation directly, we store modulations applied to a meta-learned base
network as a compressed code for the data. We further quantize and entropy code
these modulations, leading to large compression gains while reducing encoding
time by two orders of magnitude compared to baselines. We empirically
demonstrate the effectiveness of our method by compressing various data
modalities, from images to medical and climate data.

21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an