DeepAI
Log In Sign Up

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

02/27/2018
by   Amjad Almahairi, et al.
0

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.

READ FULL TEXT

page 7

page 8

07/18/2021

Tools for Analysis of Shannon-Kotel'nikov Mappings

This document summarizes results on S-K mappings obtained since 2017....
05/24/2021

Two-to-one mappings and involutions without fixed points over _2^n

In this paper, two-to-one mappings and involutions without any fixed poi...
12/14/2020

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

Cycle-consistent training is widely used for jointly learning a forward ...
06/07/2022

DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings

The existence of multiple load-solution mappings of non-convex AC-OPF pr...
07/12/2019

Equiprobable mappings in weighted constraint grammars

We show that MaxEnt is so rich that it can distinguish between any two d...
05/08/2018

Phoneme-to-viseme mappings: the good, the bad, and the ugly

Visemes are the visual equivalent of phonemes. Although not precisely de...
08/31/2020

Langevin Cooling for Domain Translation

Domain translation is the task of finding correspondence between two dom...

Code Repositories