[Lisa_seminaires] [mila-tous] [Tea Talk] Yaroslav Ganin (MILA) Fri June 15 10:30AM AA3195

Michael Noukhovitch mnoukhov at gmail.com
Ven 15 Juin 09:59:04 EDT 2018


Reminder, this is in 30 minutes!

On Thu, Jun 14, 2018, 09:57 xavier bouthillier <xavier.bouthillier at gmail.com>
wrote:

> Small note, due to the transition to lab-organisation accounts on
> BlueJeans the new link for streaming is this one
> https://mila.bluejeans.com/1365483656/webrtc
>
> On Tue, Jun 12, 2018 at 1:35 PM, Michael Noukhovitch <mnoukhov at gmail.com>
> wrote:
>
>> This week we have our very own Yaroslav Ganin giving a talk on the work
>> he did on internship at DeepMind on Friday June 15th at 10:30AM in room
>> AA3195
>>
>> This talk may be streamed as usual here
>> <https://bluejeans.com/809027115/webrtc>
>> And you can sign up to meet the speaker by messaging him on slack
>>
>> If you don't come to the talk, be prepared for a strongly reinforced
>> adversary in the form of me!
>> Michael
>>
>> *TITLE* Synthesizing Programs for Images using Reinforced Adversarial
>> Learning
>>
>> *KEYWORDS *GAN, Reinforcement Learning, Unsupervised Learning
>>
>> *ABSTRACT *
>> Advances in deep generative networks have led to impressive results in
>> recent years. Nevertheless, such models can often waste their capacity on
>> the minutiae of datasets, presumably due to weak inductive biases in their
>> decoders. This is where graphics engines may come in handy since they
>> abstract away low-level details and represent images as high-level
>> programs. Current methods that combine deep learning and renderers are
>> limited by hand-crafted likelihood or distance functions, a need for large
>> amounts of supervision, or difficulties in scaling their inference
>> algorithms to richer datasets. To mitigate these issues, we present SPIRAL,
>> an adversarially trained agent that generates a program which is executed
>> by a graphics engine to interpret and sample images. The goal of this agent
>> is to fool a discriminator network that distinguishes between real and
>> rendered data, trained with a distributed reinforcement learning setup
>> without any supervision. A surprising finding is that using the
>> discriminator’s output as a reward signal is the key to allow the agent to
>> make meaningful progress at matching the desired output rendering. To the
>> best of our knowledge, this is the first demonstration of an end-to-end,
>> unsupervised and adversarial inverse graphics agent on challenging real
>> world (MNIST, Omniglot, CelebA) and synthetic 3D datasets
>>
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