What Regularized Auto-Encoders Learn from the Data Generating Distribution

18 November 2012

Papers citing "What Regularized Auto-Encoders Learn from the Data Generating Distribution"

10 / 10 papers shown

Title
$Controllable Unlearning for Image-to-Image Generative Models via $\varepsilon$-Constrained Optimization$ Controllable Unlearning for Image-to-Image Generative Models via $\varepsilon$ -Constrained Optimization Xiaohua Feng Chao-Jun Chen Yuyuan Li Lulu Zhang Longfei Li Jun Zhou Xiaolin Zheng MU 98 0 0 20 Feb 2025
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization Yao Ni Shan Zhang Piotr Koniusz 365 6 0 25 Sep 2024
A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion Xiaoli Zhang Liying Wang Libo Zhao Xiongfei Li Siwei Ma 96 0 0 11 Jun 2024
Enabling Uncertainty Estimation in Iterative Neural Networks Nikita Durasov Doruk Öner Jonathan Donier Hieu M. Le Pascal Fua UQCV 129 8 0 25 Mar 2024
Utility Theory of Synthetic Data Generation Shi Xu W. Sun Guang Cheng 117 5 0 17 May 2023
Variational Inference using Implicit Distributions Ferenc Huszár DRL GAN 105 135 0 27 Feb 2017
Amortised MAP Inference for Image Super-resolution C. Sønderby Jose Caballero Lucas Theis Wenzhe Shi Ferenc Huszár 70 435 0 14 Oct 2016
A Theory of Generative ConvNet Jianwen Xie Yang Lu Song-Chun Zhu Ying Nian Wu DiffM GAN 76 320 0 10 Feb 2016
Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio L. Yao Guillaume Alain Pascal Vincent 84 540 0 29 May 2013
Better Mixing via Deep Representations Yoshua Bengio Grégoire Mesnil Yann N. Dauphin Salah Rifai 62 339 0 18 Jul 2012