337 citations · 340 across the 12 of their papers we have counts for
6 papers · 1 filter
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions
Mahyar Khayatkhoei, Wael AbdAlmageed
Precision and Recall are two prominent metrics of generative performance, which were proposed to separately measure the fidelity and diversity of generative models. Given their cen…
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representati…
Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors
Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them. However, those tw…
Invariant Representations through Adversarial Forgetting
Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg +2
We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechan…
Unified Adversarial Invariance
Ayush Jaiswal, Yue Wu, Wael AbdAlmageed +1
We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can ad…
Unsupervised Adversarial Invariance
Ayush Jaiswal, Yue Wu, Wael AbdAlmageed +1
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from le…