Dataset Groups Activity Stream VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder The dataset used in the paper is not explicitly described, but it is mentioned that the authors used six datasets from three different NLG tasks. BibTex: @dataset{Yueen_Ma_and_Dafeng_Chi_and_Jingjing_Li_and_Kai_Song_and_Yuzheng_Zhuang_and_Irwin_King_2024, abstract = {The dataset used in the paper is not explicitly described, but it is mentioned that the authors used six datasets from three different NLG tasks.}, author = {Yueen Ma and Dafeng Chi and Jingjing Li and Kai Song and Yuzheng Zhuang and Irwin King}, doi = {10.57702/uyxrx1df}, institution = {No Organization}, keyword = {'Generative Diversity', 'InfoGAN', 'Variational Autoencoder'}, month = {dec}, publisher = {TIB}, title = {VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder}, url = {https://service.tib.eu/ldmservice/dataset/volta--improving-generative-diversity-by-variational-mutual-information-maximizing-autoencoder}, year = {2024} }
