Message Passing Multi-Agent GANs

Communicating and sharing intelligence among agents is an important facet of achieving Artificial General Intelligence. As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs). While GANs have recently been shown to be very effective for image generation and other tasks, these networks have been limited to mostly single generator-discriminator networks. We show that we can obtain multi-agent GANs that communicate through message passing to achieve better image generation. The objectives of the individual agents in this framework are two fold: a co-operation objective and a competing objective. The co-operation objective ensures that the message sharing mechanism guides the other generator to generate better than itself while the competing objective encourages each generator to generate better than its counterpart. We analyze and visualize the messages that these GANs share among themselves in various scenarios. We quantitatively show that the message sharing formulation serves as a regularizer for the adversarial training. Qualitatively, we show that the different generators capture different traits of the underlying data distribution.

[1]  Rob Fergus,et al.  Learning Multiagent Communication with Backpropagation , 2016, NIPS.

[2]  Michael Cogswell,et al.  Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles , 2016, NIPS.

[3]  Yaroslav Bulatov,et al.  Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks , 2013, ICLR.

[4]  Ming-Yu Liu,et al.  Coupled Generative Adversarial Networks , 2016, NIPS.

[5]  Aaron C. Courville,et al.  Adversarially Learned Inference , 2016, ICLR.

[6]  Sridhar Mahadevan,et al.  Generative Multi-Adversarial Networks , 2016, ICLR.

[7]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[8]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Alexei A. Efros,et al.  Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.

[11]  William T. Freeman,et al.  On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs , 2001, IEEE Trans. Inf. Theory.

[12]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[13]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[14]  Bernt Schiele,et al.  Generative Adversarial Text to Image Synthesis , 2016, ICML.

[15]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[16]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[17]  Léon Bottou,et al.  Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.

[18]  Sebastian Nowozin,et al.  f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.

[19]  Antonio Torralba,et al.  Generating Videos with Scene Dynamics , 2016, NIPS.

[20]  Navdeep Jaitly,et al.  Adversarial Autoencoders , 2015, ArXiv.

[21]  Alexei A. Efros,et al.  Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Alexander Peysakhovich,et al.  Multi-Agent Cooperation and the Emergence of (Natural) Language , 2016, ICLR.

[23]  Martín Abadi,et al.  Learning to Protect Communications with Adversarial Neural Cryptography , 2016, ArXiv.

[24]  Yang Cai,et al.  On minmax theorems for multiplayer games , 2011, SODA '11.

[25]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[26]  Shimon Whiteson,et al.  Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.

[27]  Jiajun Wu,et al.  Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.

[28]  Amitabha Mukerjee,et al.  Contextual RNN-GANs for Abstract Reasoning Diagram Generation , 2016, AAAI.