Given a short query with keyphrases about a research idea in 2017 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 novel data-free knowledge distillation approach that is able to compress deep neural networks trained on large-scale

Keyphrases: image classification images

Dataset description:
GoPro: The GoPro dataset for deblurring consists of 3,214 blurred images with the size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. The dataset consists of pairs of a realistic blurry image and the corresponding ground truth shapr image that are obtained by a high-speed camera.
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.
30MQA: An enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions.
Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison: The Evaluation framework of Raganato et al. 2017 includes two training sets (SemCor-Miller et al., 1993- and OMSTI-Taghipour and Ng, 2015-) and five test sets from the Senseval/SemEval series (Edmonds and Cotton, 2001; Snyder and Palmer, 2004; Pradhan et al., 2007; Navigli et al., 2013; Moro and Navigli, 2015), standardized to the same format and sense inventory (i.e.

Options:- A: GoPro  B: CelebA  C: 30MQA  D: Word Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison
CelebA