Given a short query with keyphrases about a research idea in 2018 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A compression system for image data which allows to smoothly interpolate between learning a generative model

Keyphrases: distribution-preserving lossy compression

Dataset description:
CelebA: CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age.
NYU-VP: NYU-VP is a new dataset for multi-model fitting, vanishing point (VP) estimation in this case. Each image is annotated with up to eight vanishing points, and pre-extracted line segments are provided which act as data points for a robust estimator.
ORVS: The ORVS dataset has been newly established as a collaboration between the computer science and visual-science departments at the University of Calgary. This dataset contains 49 images (42 training and seven testing images) collected from a clinic in Calgary-Canada.
Sentence Compression: Sentence Compression is a dataset where the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence where supervised systems which require a structural alignment between the input and output can be successfully trained.

Options:- A: CelebA  B: NYU-VP  C: ORVS  D: Sentence Compression
CelebA