In this homework, you will need to extend the idea of CycleGAN to multiple-domain scenarios. We may call these types of models: Bi-CycleGANs or Tri-CycleGANs, or even Multi-CycleGANs.

[12/3/2017 update] This homework is a little bit different from the paper here. The paper aims to generate images with diverse colors/textures from single image and a latent code, while we want you to generate images in multiple modalities from single image (and a latent code).

To make it clear:

• The paper from JY-Zhu el at. formulate two cycles between: z -> B' -> z, B -> z' -> B', where Z is the latent code, B is the generated output.
• We formulate the cycles to be: A -> B' -> A', B -> C' -> B', where A, B, and C are images from different modalities.

[11/30/2017 update] The related nips paper is here: https://arxiv.org/abs/1711.11586 and official implementation should be released soon.

CycleGAN is designed for Unpaired Image-to-Image Translation and many interesting applications of CycleGAN can be found in the paper and on their project website. Here we want to make good use of it and extend it to multiple cycles between different modalities.

Motivation: with CycleGAN, we can better transfer the styles of images from domain A to domain B and vice versa without unpaired data. What will it be if we have two cycles (A, B) and (B, C) where B is the common modality shared by the two cycles? Your task is to find some scenarios and related datasets that can demonstrate the idea and the advantages of Bi-CycleGAN for image-to-image-to-image transfer. 🔥

For confused students, here are some concrete examples:

Cycle 1: A ←→ B
Cycle 2: B ←→ C

B is the common domain (or common modality) shared by the two cycles.

Examples:

[Automatically generated dataset]
A: MNIST
B: Inverted MNIST (black->white, white->black)
C: Red-Green MNIST (black->red, white->green)

Or

A: An RGB image containing a person
B: Depth map
C: Keypoints (body joints)

Or

A: object captured from 0 degree
B: object captured from 30 degree
C: object captured from 60 degree
[Can be obtained by ShapeNet]

More other examples might be inspired by here

Note that the relation (correspondence) among multiple modalities is never provided (if you generate the dataset yourselves, you need to shuffle the dataset. Although it's still implicitly paired, it's fine for this homework)

• Torch implementation: here
• Pytorch implementation: here

For other implementation, please refer to their project page

• [75%] Find a scenario and related datasets to demonstrate the idea of BicycleGAN.  - You may simply train two CycleGANs separately using the original code of CycleGAN without enforcing the consistency of the shared domain between the two cycles.
• [15%] Implement Bi-CycleGANs so that you can jointly train the two cycles with additional constraints on the consistency of the shared domain. Compare the results of separate training and joint training.
• [10%] Report
• [5%] Bonus, share you code and what you learn on github or yourpersonal blogs, such as this

• Deadline: Dec. 19 23:59, 2017
• Office hour by appointment Yuan-Hong Liao.
• Contact [email protected] for bugs report or any questions.