An information flow-preserving knowledge distillation methodology for model compression. read more of InDistill

Retrieving algorithm component combinations from online (or local) yaml resources. read more of yamlres

Provides a framework for learning text-driven generative paths in pre-trained GANs. read more of ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences

Provides a framework for anonymizing faces in public datasets using pre-trained GANs. read more of Attribute-preserving Face Dataset Anonymization via Latent Code Optimization

Provides a framework for discovering non-linear interpretable paths in pre-trained GAN latent spaces. read more of WarpedGANSpace: Finding non-linear RBF paths in GAN latent space

Provides a Neural Face Reenactment framework by leveraging the expressiveness of the StyleGAN2’s style space. read more of StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment

Provides a framework for the problem of Neural Face Reenactment using Generative Adversarial Networks (GANs). read more of HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces

Provides a video similarity learning approach using self-supervision. read more of Self-Supervised Video Similarity Learning

Provides a framework for addressing the problem of computationally efficient content-based video retrieval in large-scale datasets. read more of DnS: Distill-and-select for efficient and accurate video indexing and retrieval

Provides a framework for finding interpretable directions in the latent space of convolutional GANs. read more of Tensor Component Analysis for Interpreting the Latent Space of GANs