182 citations
6 papers
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics
Aurobrata Ghosh, Zheng Zhong, Steve Cruz +3
We consider an information theoretic approach to address the problem of identifying fake digital images. We propose an innovative method to formulate the issue of localizing manipu…
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao +4
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: 1) lack of aligned training pairs and 2) multiple poss…
Diverse Image-to-Image Translation via Disentangled Representations
Hsin-Ying Lee, Hung-Yu Tseng, Jia-Bin Huang +2
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) m…
Disconnected Manifold Learning for Generative Adversarial Networks
Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative A…
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Ananya Harsh Jha, Saket Anand, Maneesh Singh +1
Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the dis…
Unsupervised Representation Learning by Sorting Sequences
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh +1
We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating represen…