Learning Club talk by Yedid Hoshen

———————————–

Hey everyone,

On Sunday 30.5.2021 we will host Yedid Hoshen from the Hebrew University.

Please see the details below.

See you then,

Roni

——

Zoom:

https://us02web.zoom.us/j/84758320608?pwd=SWljZGxhT1lkK1c2U0YyaVVEclZFUT09

Meeting ID: 847 5832 0608

Passcode: 942440

Title:

Scaling-up Disentanglement

Abstract:

Disentangling data into attributes that are meaningful to humans is a key task in machine learning. Here, we tackle the case where supervision is available for some of the attributes but not for others. Most large-scale disentanglement methods use adversarial training which typically yields imperfect results. We will first describe a simple, generic non-adversarial method, LORD, which outperforms the corresponding GAN-based methods. The secret sauce of this method is a latent optimization-based information bottleneck. Unfortunately, LORD is unable to scale-up to high-dimensional multi-modal disentanglement tasks such as image translation. We therefore present an advanced framework, OverLORD which overcomes these issues. We show that this flexible framework covers multiple image translation settings e.g. attribute manipulation, pose-appearance translation, segmentation-guided synthesis and shape-texture transfer. In an extensive evaluation, we present significantly better disentanglement with higher translation quality and greater output diversity than state-of-the-art methods.