Contextual RNN-GANs for Abstract Reasoning Diagram Generation

TL;DR

This paper introduces Context-RNN-GANs, a novel model for generating and understanding evolving diagram sequences, achieving human-level performance in abstract reasoning and improved results in video frame prediction.

Contribution

The paper presents a new RNN-based GAN architecture for diagram sequence generation and demonstrates its effectiveness on abstract reasoning and video prediction tasks.

Findings

Performs comparably to 10th-grade humans on reasoning tasks.

Outperforms state-of-the-art in next-frame video prediction.

Shows potential for further improvements in complex pattern generation.

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.