You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: This paper proposes a similarity preserving generative adversarial network to address the unsupervised domain adaptation problem in person re

Keyphrases: person re-identification images

Dataset description:
ImageCLEF-DA: The ImageCLEF-DA dataset is a benchmark dataset for ImageCLEF 2014 domain adaptation challenge, which contains three domains: Caltech-256 (C), ImageNet ILSVRC 2012 (I) and Pascal VOC 2012 (P). For each domain, there are 12 categories and 50 images in each category.
Market-1501: Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector.
FDF: A diverse dataset of human faces, including unconventional poses, occluded faces, and a vast variability in backgrounds.
TechQA: TECHQA is a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain.
Libri-Adapt: Libri-Adapt aims to support unsupervised domain adaptation research on speech recognition models.
DukeMTMC-reID: The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras.
VisDA-2017: VisDA-2017 is a simulation-to-real dataset for domain adaptation with over 280,000 images across 12 categories in the training, validation and testing domains. The training images are generated from the same object under different circumstances, while the validation images are collected from MSCOCO..

Options:- A: ImageCLEF-DA  B: Market-1501  C: FDF  D: TechQA  E: Libri-Adapt  F: DukeMTMC-reID  G: VisDA-2017
["Market-1501", "DukeMTMC-reID"]