
HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces
Provides a framework for the problem of Neural Face Reenactment using Generative Adversarial Networks (GANs).
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Provides a framework for the problem of Neural Face Reenactment using Generative Adversarial Networks (GANs).
Provides a video similarity learning approach using self-supervision.
Provides a framework for addressing the problem of computationally efficient content-based video retrieval in large-scale datasets.
Provides a framework for finding interpretable directions in the latent space of convolutional GANs.
A framework aiming to improve generalization performance and mitigate overfitting in deep learning methodologies in automated human affect and mental state estimation by introducing a novel relational loss for multilabel regression and ordinal problems, a...
List of Deep Learning models for defecting defect on an image dataset of metal assembly pieces
Machine Learning and Deep Learning models to detect anomalies using time series data.
Model able to generate a production plan for a week given a set of orders that minimizes the monetary loss .
It is a service for defect detection and defect localization in hard metal industry
EurekAD is a service for anomaly detection in Industrial time series data.