Unsupervised Learning
Compositional Pattern Producing Networks (CPPNs) are differentiable networks that independently map (x, y) pixel coordinates to (r, g, …
Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport …
Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. …
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We …
We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by …
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also …
We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by …
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, …
The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an …