Contextual RNN-GANs for abstract reasoning diagram generation

Viveka Kulharia, Arnab Ghosh, Amitabha Mukerjee, Vinay Namboodiri, Mohit Bansal

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-RNN-GANs), where both the generator and the discriminator modules are based on contextual history (modeled as RNNs) and the adversarial discriminator guides the generator to produce realistic images for the particular time step in the image sequence. We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art.

Original languageEnglish
Pages1382-1388
Number of pages7
Publication statusPublished - 1 Jan 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, USA United States
Duration: 4 Feb 201710 Feb 2017

Conference

Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUSA United States
CitySan Francisco
Period4/02/1710/02/17

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Kulharia, V., Ghosh, A., Mukerjee, A., Namboodiri, V., & Bansal, M. (2017). Contextual RNN-GANs for abstract reasoning diagram generation. 1382-1388. Paper presented at 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, USA United States.