Timezone: »

 
Poster
On Adversarial Mixup Resynthesis
Christopher Beckham · Sina Honari · Alex Lamb · Vikas Verma · Farnoosh Ghadiri · R Devon Hjelm · Yoshua Bengio · Chris Pal

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #61
in Algorithms -- Unsupervised Learning »

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.

Keywords: Unsupervised Learning · Generative Models · Algorithms · Algorithms -> Adversarial Learning; Deep Learning -> Deep Autoencoders; Deep Learning

Author Information

Christopher Beckham (Mila)
Sina Honari (EPFL, Mila)
Alex Lamb (UMontreal (MILA))
Vikas Verma (Aalto University)
Farnoosh Ghadiri (Mila)
R Devon Hjelm (Microsoft Research)
Yoshua Bengio (Mila)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

Chris Pal (MILA, Polytechnique Montréal, Element AI)

More from the Same Authors