ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences
Provides a framework for learning text-driven generative paths in pre-trained GANs.
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Provides a framework for learning text-driven generative paths in pre-trained GANs.
Provides a framework for anonymizing faces in public datasets using pre-trained GANs.
Provides a framework for discovering non-linear interpretable paths in pre-trained GAN latent spaces.
Provides a Neural Face Reenactment framework by leveraging the expressiveness of the StyleGAN2’s style space.
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