Timezone: »
Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity.
Author Information
Jun-Yan Zhu (UC Berkeley)
Richard Zhang (University of California, Berkeley)
Deepak Pathak (UC Berkeley)
Trevor Darrell (UC Berkeley)
Alexei Efros (UC Berkeley)
Oliver Wang (Adobe Research)
Eli Shechtman (Adobe Research, US)
More from the Same Authors
-
2020 Poster: Few-shot Image Generation with Elastic Weight Consolidation »
Yijun Li · Richard Zhang · Jingwan (Cynthia) Lu · Eli Shechtman -
2020 Poster: Auxiliary Task Reweighting for Minimum-data Learning »
Baifeng Shi · Judy Hoffman · Kate Saenko · Trevor Darrell · Huijuan Xu -
2020 Poster: Space-Time Correspondence as a Contrastive Random Walk »
Allan Jabri · Andrew Owens · Alexei Efros -
2020 Oral: Space-Time Correspondence as a Contrastive Random Walk »
Allan Jabri · Andrew Owens · Alexei Efros -
2020 Poster: Swapping Autoencoder for Deep Image Manipulation »
Taesung Park · Jun-Yan Zhu · Oliver Wang · Jingwan Lu · Eli Shechtman · Alexei Efros · Richard Zhang -
2020 Poster: Fighting Copycat Agents in Behavioral Cloning from Observation Histories »
Chuan Wen · Jierui Lin · Trevor Darrell · Dinesh Jayaraman · Yang Gao -
2019 Workshop: AI for Humanitarian Assistance and Disaster Response »
Ritwik Gupta · Robin Murphy · Trevor Darrell · Eric Heim · Zhangyang Wang · Bryce Goodman · Piotr Biliński -
2019 Poster: Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller »
Pratyusha Sharma · Deepak Pathak · Abhinav Gupta -
2019 Poster: Compositional Plan Vectors »
Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine -
2019 Poster: Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity »
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros -
2019 Spotlight: Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity »
Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros -
2018 Poster: Self-Supervised Generation of Spatial Audio for 360° Video »
Pedro Morgado · Nuno Nvasconcelos · Timothy Langlois · Oliver Wang -
2018 Poster: Speaker-Follower Models for Vision-and-Language Navigation »
Daniel Fried · Ronghang Hu · Volkan Cirik · Anna Rohrbach · Jacob Andreas · Louis-Philippe Morency · Taylor Berg-Kirkpatrick · Kate Saenko · Dan Klein · Trevor Darrell -
2016 Workshop: Machine Learning for Intelligent Transportation Systems »
Li Erran Li · Trevor Darrell -
2014 Poster: Do Convnets Learn Correspondence? »
Jonathan L Long · Ning Zhang · Trevor Darrell -
2014 Poster: LSDA: Large Scale Detection through Adaptation »
Judy Hoffman · Sergio Guadarrama · Eric Tzeng · Ronghang Hu · Jeff Donahue · Ross Girshick · Trevor Darrell · Kate Saenko -
2014 Poster: Weakly-supervised Discovery of Visual Pattern Configurations »
Hyun Oh Song · Yong Jae Lee · Stefanie Jegelka · Trevor Darrell -
2013 Poster: Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies »
Yangqing Jia · Joshua T Abbott · Joseph L Austerweil · Tom Griffiths · Trevor Darrell -
2012 Poster: Learning with Recursive Perceptual Representations »
Oriol Vinyals · Yangqing Jia · Li Deng · Trevor Darrell -
2012 Poster: Timely Object Recognition »
Sergey K Karayev · Tobi Baumgartner · Mario Fritz · Trevor Darrell -
2011 Workshop: Integrating Language and Vision »
Raymond Mooney · Trevor Darrell · Kate Saenko -
2011 Poster: Heavy-tailed Distances for Gradient Based Image Descriptors »
Yangqing Jia · Trevor Darrell -
2010 Poster: Factorized Latent Spaces with Structured Sparsity »
Yangqing Jia · Mathieu Salzmann · Trevor Darrell -
2010 Poster: Size Matters: Metric Visual Search Constraints from Monocular Metadata »
Mario J Fritz · Kate Saenko · Trevor Darrell -
2009 Poster: Learning to Hash with Binary Reconstructive Embeddings »
Brian Kulis · Trevor Darrell -
2009 Spotlight: Learning to Hash with Binary Reconstructive Embeddings »
Brian Kulis · Trevor Darrell -
2009 Poster: An Additive Latent Feature Model for Transparent Object Recognition »
Mario J Fritz · Michael J Black · Gary R Bradski · Trevor Darrell -
2009 Poster: Filtering Abstract Senses From Image Search Results »
Kate Saenko · Trevor Darrell -
2009 Oral: An Additive Latent Feature Model for Transparent Object Recognition »
Mario J Fritz · Michael J Black · Gary R Bradski · Trevor Darrell -
2008 Poster: Unsupervised Learning of Visual Sense Models for Polysemous Words »
Kate Saenko · Trevor Darrell -
2008 Spotlight: Unsupervised Learning of Visual Sense Models for Polysemous Words »
Kate Saenko · Trevor Darrell -
2006 Poster: Approximate Correspondences in High Dimensions »
Kristen Grauman · Trevor Darrell -
2006 Spotlight: Approximate Correspondences in High Dimensions »
Kristen Grauman · Trevor Darrell