Search Results for author:
Found 9 papers, 5 papers with code
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose inscrutable dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability.
Parts of Speech-Grounded Subspaces in Vision-Language Models
Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks.
ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences
This work addresses the problem of discovering non-linear interpretable paths in the latent space of pre-trained GANs in a model-agnostic manner.
PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs.
Cluster-guided Image Synthesis with Unconditional Models
The results highlight the ability of our approach to condition image generation on attributes like gender, pose and hair style on faces, as well as a variety of features on different object classes.
Tensor Component Analysis for Interpreting the Latent Space of GANs
This paper addresses the problem of finding interpretable directions in the latent space of pre-trained Generative Adversarial Networks (GANs) to facilitate controllable image synthesis.
Tensor Methods in Computer Vision and Deep Learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions.
Enhancing Facial Data Diversity with Style-based Face Aging
By evaluating on several age-annotated datasets in both single- and cross-database experiments, we show that the proposed method outperforms state-of-the-art algorithms for age transfer, especially in the case of age groups that lie in the tails of the label distribution.
Adversarial Learning of Disentangled and Generalizable Representations for Visual Attributes
Recently, a multitude of methods for image-to-image translation have demonstrated impressive results on problems such as multi-domain or multi-attribute transfer.
