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Diagnosing and Enhancing VAE Models

14 March 2019

Papers citing "Diagnosing and Enhancing VAE Models"

50 / 242 papers shown

Title
VAE Approximation Error: ELBO and Exponential Families Alexander Shekhovtsov D. Schlesinger B. Flach DRL 68 16 0 18 Feb 2021
Preventing Oversmoothing in VAE via Generalized Variance Parameterization Yuhta Takida Wei-Hsiang Liao Chieh-Hsin Lai Toshimitsu Uesaka Shusuke Takahashi Yuki Mitsufuji DRL 94 15 0 17 Feb 2021
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders Harshana Habaragamuwa Y. Oishi Kenichi Tanaka 98 9 0 05 Feb 2021
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild Yizhou Zhou Chong Luo Xiaoyan Sun Zhengjun Zha Wenjun Zeng VGen 28 2 0 28 Jan 2021
Cauchy-Schwarz Regularized Autoencoder Linh-Tam Tran Maja Pantic M. Deisenroth DRL BDL 75 17 0 06 Jan 2021
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions Daniel Stanley Tan Yi-Chun Chen Trista Pei-chun Chen Wei-Chao Chen UQCV 81 57 0 29 Dec 2020
AVAE: Adversarial Variational Auto Encoder Antoine Plumerault Hervé Le Borgne C´eline Hudelot GAN DRL 61 16 0 21 Dec 2020
Taming Transformers for High-Resolution Image Synthesis Patrick Esser Robin Rombach Bjorn Ommer ViT 141 3,015 0 17 Dec 2020
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings Qipeng Guo Zhijing Jin Ziyu Wang Xipeng Qiu Weinan Zhang Jun Zhu Zheng Zhang David Wipf 65 13 0 14 Dec 2020
Comparison of Anomaly Detectors: Context Matters V. Škvára Jan Francå Matěj Zorek Tomás Pevný Václav Smídl 62 9 0 11 Dec 2020
Generative Capacity of Probabilistic Protein Sequence Models Francisco McGee Quentin Novinger R. Levy Vincenzo Carnevale A. Haldane 81 34 0 03 Dec 2020
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images R. Child BDL VLM 192 353 0 20 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 153 106 0 03 Nov 2020
ControlVAE: Tuning, Analytical Properties, and Performance Analysis Huajie Shao Zhisheng Xiao Shuochao Yao Aston Zhang Shengzhong Liu Tarek Abdelzaher DRL 99 16 0 31 Oct 2020
GENs: Generative Encoding Networks Surojit Saha Shireen Y. Elhabian Ross T. Whitaker GAN 56 9 0 28 Oct 2020
Further Analysis of Outlier Detection with Deep Generative Models Ziyu Wang Bin Dai David Wipf Jun Zhu 76 40 0 25 Oct 2020
Plug and Play Autoencoders for Conditional Text Generation Florian Mai Nikolaos Pappas Ivan Montero Noah A. Smith U. Washington 109 37 0 06 Oct 2020
A Contrastive Learning Approach for Training Variational Autoencoder Priors J. Aneja Alex Schwing Jan Kautz Arash Vahdat DRL 122 83 0 06 Oct 2020
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein Khai Nguyen S. Nguyen Nhat Ho Tung Pham Hung Bui 117 21 0 05 Oct 2020
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models Zhisheng Xiao Karsten Kreis Jan Kautz Arash Vahdat 116 124 0 01 Oct 2020
Disentangled Neural Architecture Search Xinyue Zheng Peng Wang Qigang Wang Zhongchao Shi AI4CE 57 4 0 24 Sep 2020
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning Karsten Roth Timo Milbich Bjorn Ommer Joseph Paul Cohen Marzyeh Ghassemi FedML 141 17 0 17 Sep 2020
Generative models with kernel distance in data space Szymon Knop Marcin Mazur Przemysław Spurek Jacek Tabor Igor T. Podolak GAN SyDa 61 11 0 15 Sep 2020
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology Maxime W. Lafarge J. Pluim M. Veta DRL 46 8 0 26 Aug 2020
Anomaly localization by modeling perceptual features David Dehaene P. Eline DRL 70 42 0 12 Aug 2020
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs Robin Rombach Patrick Esser Bjorn Ommer 88 16 0 04 Aug 2020
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding Akira Nakagawa Keizo Kato Taiji Suzuki DRL 80 9 0 30 Jul 2020
Generalizing Variational Autoencoders with Hierarchical Empirical Bayes Wei Cheng Gregory Darnell Sohini Ramachandran Lorin Crawford BDL 13 2 0 20 Jul 2020
Relaxed-Responsibility Hierarchical Discrete VAEs M. Willetts Xenia Miscouridou Stephen J. Roberts Chris Holmes BDL DRL 86 5 0 14 Jul 2020
Bridging Maximum Likelihood and Adversarial Learning via $α$ -Divergence Miaoyun Zhao Yulai Cong Shuyang Dai Lawrence Carin GAN 58 10 0 13 Jul 2020
Self-Reflective Variational Autoencoder Ifigeneia Apostolopoulou Elan Rosenfeld A. Dubrawski OOD BDL DRL 52 0 0 10 Jul 2020
Benefiting Deep Latent Variable Models via Learning the Prior and Removing Latent Regularization Rogan Morrow Wei-Chen Chiu DRL 31 0 0 07 Jul 2020
Sliced Iterative Normalizing Flows B. Dai U. Seljak 89 37 0 01 Jul 2020
Simple and Effective VAE Training with Calibrated Decoders Oleh Rybkin Kostas Daniilidis Sergey Levine 105 95 0 23 Jun 2020
Normalizing Flows Across Dimensions Edmond Cunningham Renos Zabounidis Abhinav Agrawal Ina Fiterau Daniel Sheldon DRL 66 26 0 23 Jun 2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma Sebastian Tschiatschek José Miguel Hernández-Lobato Richard Turner Cheng Zhang DRL VLM 81 69 0 21 Jun 2020
Constraining Variational Inference with Geometric Jensen-Shannon Divergence J. Deasy Nikola Simidjievski Pietro Lio DRL 91 30 0 18 Jun 2020
Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy Zhisheng Xiao Qing Yan Y. Amit DRL 53 8 0 15 Jun 2020
Structure by Architecture: Structured Representations without Regularization Felix Leeb Giulia Lanzillotta Yashas Annadani M. Besserve Stefan Bauer Bernhard Schölkopf OOD CML 89 8 0 14 Jun 2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 110 116 0 11 Jun 2020
End-to-end Sinkhorn Autoencoder with Noise Generator Kamil Deja Jan Dubiñski Piotr W. Nowak S. Wenzel Tomasz Trzciñski SyDa 60 23 0 11 Jun 2020
A Generalised Linear Model Framework for $β$ -Variational Autoencoders based on Exponential Dispersion Families Robert Sicks R. Korn Stefanie Schwaar 68 12 0 11 Jun 2020
To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs A. Mondal Himanshu Asnani Parag Singla A. Prathosh DRL 35 1 0 10 Jun 2020
Probabilistic Autoencoder Vanessa Böhm U. Seljak UQCV BDL DRL 81 32 0 09 Jun 2020
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization Andrew Stirn David A. Knowles DRL 92 10 0 08 Jun 2020
Network-to-Network Translation with Conditional Invertible Neural Networks Robin Rombach Patrick Esser Bjorn Ommer 40 3 0 27 May 2020
Learning and Inference in Imaginary Noise Models Saeed Saremi BDL DRL 83 2 0 18 May 2020
Data Augmentation for Deep Candlestick Learner Chia-Ying Tsao Jun-Hao Chen Samuel Yen-Chi Chen Yun-Cheng Tsai 24 1 0 14 May 2020
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders Saeid Asgari Taghanaki Mohammad Havaei Alex Lamb Aditya Sanghi Aram Danielyan Tonya Custis DRL 74 7 0 12 May 2020
A Disentangling Invertible Interpretation Network for Explaining Latent Representations Patrick Esser Robin Rombach Bjorn Ommer 62 88 0 27 Apr 2020