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

xavier bouthillier xavier.bouthillier at gmail.com
Sam 16 Juin 14:06:43 EDT 2018


Here is the recording

https://bluejeans.com/s/jy5Rl/

And the slides

https://docs.google.com/presentation/d/1ZWFDcuJqQI4HmrkwtBPzV16WNr2Lq7Xf2mPqAqHV2Jc/edit?usp=sharing

On Fri, Jun 15, 2018 at 9:59 AM, Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> 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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