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: I want to improve semi-supervised learning performance.

Keyphrases: image classification images

Dataset description:
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).
AFLW: The Annotated Facial Landmarks in the Wild (AFLW) is a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25K faces are annotated with up to 21 landmarks per image.
DailyDialog++: Consists of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context.
VoxPopuli: VoxPopuli is a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning.
DCASE 2018 Task 4: DCASE2018 Task 4 is a dataset for large-scale weakly labeled semi-supervised sound event detection in domestic environments. The data are YouTube video excerpts focusing on domestic context which could be used for example in ambient assisted living applications.
ExtremeWeather: Encourages machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.

Options:- A: CIFAR-10  B: AFLW  C: DailyDialog++  D: VoxPopuli  E: DCASE 2018 Task 4  F: ExtremeWeather
["CIFAR-10", "AFLW"]