Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation – A recently proposed method for unsupervised translation (OSMT) is based on the idea of learning a deep neural network to translate objects by identifying the regions in which they should be localized. The OSMT algorithm learns the region that best localizes the object and then translates the object by means of a recurrent neural network. The underlying feature sets are learned from the model, and hence the proposed OSMT method learns the representation of the objects in the feature set at hand. We demonstrate that the proposed method outperforms state-of-the-art unsupervised translation methods on an OSMT task.
In this paper, we explore multiscale representation of facial expressions with expressive power and demonstrate results on multi-scale face estimation from four popular metrics: facial expression, facial expression volume, expression pose and face pose estimation. Experiments with several facial expression datasets (e.g., CelebA, CelebACG and CelebACG) show that the proposed approach has superior performance than three previous unsupervised and supervised approaches for multi-scale representation.
The NLP Level with n Word Segments
Bayesian Inference With Linear Support Vector Machines
Feature Selection with Generative Adversarial Networks Improves Neural Machine Translation
Recovering Questionable Clause Representations from Question-Answer Data
The Power of Multiscale Representation for Accurate 3D Hand Pose EstimationIn this paper, we explore multiscale representation of facial expressions with expressive power and demonstrate results on multi-scale face estimation from four popular metrics: facial expression, facial expression volume, expression pose and face pose estimation. Experiments with several facial expression datasets (e.g., CelebA, CelebACG and CelebACG) show that the proposed approach has superior performance than three previous unsupervised and supervised approaches for multi-scale representation.