[AI Seminar] ai-seminar-announce Digest, Vol 77, Issue 2

Adams Wei Yu weiyu at cs.cmu.edu
Mon Oct 9 08:08:46 EDT 2017


A gentle reminder that the talk will happen tomorrow (Tuesday) noon at NSH
1507.

On Sun, Oct 8, 2017 at 9:00 AM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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>    1.  AI Seminar sponsored by Apple -- Chun-Liang Li --        October 10
>       (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 7 Oct 2017 16:18:31 -0700
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Subject: [AI Seminar] AI Seminar sponsored by Apple -- Chun-Liang Li
>         --      October 10
> Message-ID:
>         <CABzq7erc-_U2G4PyYaLdB0QaZsgyJBP2r8BaDKjP
> zRvGiKYZYw at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear faculty and students,
>
> We look forward to seeing you next Tuesday, October 10, at noon in NSH 1507
> (unusual place) for AI Seminar sponsored by Apple. To learn more about the
> seminar series, please visit the AI Seminar webpage
> <http://www.cs.cmu.edu/~aiseminar/>.
>
> On Tuesday, Chun-Liang Li <http://www.cs.cmu.edu/~chunlial/> will give the
> following talk:
>
> Title: MMD GAN: Towards Deeper Understanding of Moment Matching Network
>
> Abstract:
>
> Generative moment matching network (GMMN) is a deep generative model that
> differs from Generative Adversarial Network (GAN) by replacing the
> discriminator in GAN with a two-sample test based on kernel maximum mean
> discrepancy (MMD). Although some theoretical guarantees of MMD have been
> studied, the empirical performance of GMMN is still not as competitive as
> that of GAN on challenging and large benchmark datasets. The computational
> efficiency of GMMN is also less desirable in comparison with GAN, partially
> due to its requirement for a rather large batch size during the training.
> In this paper, we propose to improve both the model expressiveness of GMMN
> and its computational efficiency by introducing adversarial kernel learning
> techniques, as the replacement of a fixed Gaussian kernel in the original
> GMMN. The new approach combines the key ideas in both GMMN and GAN, hence
> we name it MMD-GAN. The new distance measure in MMD-GAN is a meaningful
> loss that enjoys the advantage of weak topology and can be optimized via
> gradient descent with relatively small batch sizes. In our evaluation on
> multiple benchmark datasets, including MNIST, CIFAR- 10, CelebA and LSUN,
> the performance of MMD-GAN significantly outperforms GMMN, and is
> competitive with other representative GAN works.
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