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: We propose a hybrid architecture combining a convolutional network and an off-the-shelf retrieval engine. Our

Keyphrases: adversarial attack defense image

Dataset description:
MobiBits: A novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device.
MERL Shopping: MERL Shopping is a dataset for training and testing action detection algorithms. The MERL Shopping Dataset consists of 106 videos, each of which is a sequence about 2 minutes long.
THEODORE: Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes.
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
NAS-Bench-101: NAS-Bench-101 is the first public architecture dataset for NAS research. To build NASBench-101, the authors carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures.
CIFAR-10: The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck).
SUM: SUM is a new benchmark dataset of semantic urban meshes which covers about 4 km2 in Helsinki (Finland), with six classes: Ground, Vegetation, Building, Water, Vehicle, and Boat. The authors used Helsinki 3D textured meshes as input and annotated them as a benchmark dataset of semantic urban meshes.

Options:- A: MobiBits  B: MERL Shopping  C: THEODORE  D: ImageNet  E: NAS-Bench-101  F: CIFAR-10  G: SUM
["ImageNet", "CIFAR-10"]