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Hierarchical VAEs Know What They Don't Know
v1v2v3v4v5v6v7 (latest)

Hierarchical VAEs Know What They Don't Know

International Conference on Machine Learning (ICML), 2021
16 February 2021
Jakob Drachmann Havtorn
J. Frellsen
Søren Hauberg
Lars Maaløe
    DRL
ArXiv (abs)PDFHTMLGithub (29★)

Papers citing "Hierarchical VAEs Know What They Don't Know"

50 / 50 papers shown
Exploring bidirectional bounds for minimax-training of Energy-based models
Exploring bidirectional bounds for minimax-training of Energy-based modelsInternational Journal of Computer Vision (IJCV), 2025
Cong Geng
Jia Wang
Li Chen
Zhiyong Gao
J. Frellsen
Søren Hauberg
295
0
0
05 Jun 2025
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition
Soham Khisa
Avijoy Chakma
314
0
0
08 May 2025
Autoencoders for Anomaly Detection are Unreliable
Autoencoders for Anomaly Detection are Unreliable
Roel Bouman
Tom Heskes
417
12
0
23 Jan 2025
Resultant: Incremental Effectiveness on Likelihood for Unsupervised
  Out-of-Distribution Detection
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
Yewen Li
Chaojie Wang
Xiaobo Xia
Xu He
Ruyi An
Dong Li
Tongliang Liu
Bo An
Xinrun Wang
OODD
266
0
0
05 Sep 2024
Low-Quality Image Detection by Hierarchical VAE
Low-Quality Image Detection by Hierarchical VAE
Tomoyasu Nanaumi
Kazuhiko Kawamoto
Hiroshi Kera
304
2
0
20 Aug 2024
A Non-negative VAE:the Generalized Gamma Belief Network
A Non-negative VAE:the Generalized Gamma Belief Network
Zhibin Duan
Tiansheng Wen
Muyao Wang
Bo Chen
Mingyuan Zhou
BDL
352
3
0
06 Aug 2024
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Hiroshi Takahashi
Tomoharu Iwata
Atsutoshi Kumagai
Yuuki Yamanaka
407
4
0
29 May 2024
Learning Latent Space Hierarchical EBM Diffusion Models
Learning Latent Space Hierarchical EBM Diffusion Models
Jiali Cui
Tian Han
DiffM
514
7
0
22 May 2024
A Geometric Explanation of the Likelihood OOD Detection Paradox
A Geometric Explanation of the Likelihood OOD Detection Paradox
Hamidreza Kamkari
Brendan Leigh Ross
Jesse C. Cresswell
M. Volkovs
Rahul G. Krishnan
Gabriel Loaiza-Ganem
OODD
433
19
0
27 Mar 2024
Image-based Novel Fault Detection with Deep Learning Classifiers using
  Hierarchical Labels
Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels
N. Sergin
Jiayu Huang
Tzyy-Shuh Chang
Hao Yan
263
3
0
26 Mar 2024
Approximations to the Fisher Information Metric of Deep Generative
  Models for Out-Of-Distribution Detection
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
Sam Dauncey
Chris Holmes
Christopher Williams
Fabian Falck
405
2
0
03 Mar 2024
Feature Density Estimation for Out-of-Distribution Detection via
  Normalizing Flows
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows
Evan Cook
Marc-Antoine Lavoie
Steven L. Waslander
OODD
365
4
0
09 Feb 2024
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its
  Application to OOD Detection
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detection
Sicong Huang
Jiawei He
Kry Yik-Chau Lui
239
0
0
10 Jan 2024
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's
  t and Power Divergence
t3t^3t3-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power DivergenceInternational Conference on Learning Representations (ICLR), 2023
Juno Kim
Jaehyuk Kwon
Mincheol Cho
Hyunjong Lee
Joong-Ho Won
318
10
0
02 Dec 2023
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class
  Incremental Learning
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental LearningInternational Conference on Information and Knowledge Management (CIKM), 2023
Anton Lee
Yaqian Zhang
Heitor Murilo Gomes
Nikolaos Perrakis
Bernhard Pfahringer
CLL
267
5
0
30 Oct 2023
NPCL: Neural Processes for Uncertainty-Aware Continual Learning
NPCL: Neural Processes for Uncertainty-Aware Continual LearningNeural Information Processing Systems (NeurIPS), 2023
Saurav Jha
Dong Gong
He Zhao
Lina Yao
CLLBDL
249
23
0
30 Oct 2023
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion
  Probabilistic Models
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic ModelsBritish Machine Vision Conference (BMVC), 2023
Joseph Goodier
Neill D.F. Campbell
DiffM
222
11
0
26 Oct 2023
Learning Hierarchical Features with Joint Latent Space Energy-Based
  Prior
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior
Jiali Cui
Ying Nian Wu
Tian Han
BDL
222
11
0
14 Oct 2023
How to train your VAE
How to train your VAEInternational Conference on Information Photonics (ICIP), 2023
Mariano Rivera
DRL
216
2
0
22 Sep 2023
Learning Nonparametric High-Dimensional Generative Models: The
  Empirical-Beta-Copula Autoencoder
Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder
Maximilian Coblenz
Oliver Grothe
Fabian Kächele
SyDaDRL
294
0
0
18 Sep 2023
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs
  with Variational Autoencoder
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational Autoencoder
Zezhen Zeng
Bin Liu
OODD
395
1
0
05 Sep 2023
Variational Autoencoding of Dental Point Clouds
Variational Autoencoding of Dental Point Clouds
J. Z. Ye
Thomas Orkild
P. Søndergaard
Søren Hauberg
3DPC
496
4
0
20 Jul 2023
Unsupervised 3D out-of-distribution detection with latent diffusion
  models
Unsupervised 3D out-of-distribution detection with latent diffusion modelsInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
M. Graham
W. H. Pinaya
P. Wright
Petru-Daniel Tudosiu
Y. Mah
...
H. Jäger
D. Werring
P. Nachev
Sebastien Ourselin
M. Jorge Cardoso
DiffMMedIm
216
26
0
07 Jul 2023
Learning Unnormalized Statistical Models via Compositional Optimization
Learning Unnormalized Statistical Models via Compositional OptimizationInternational Conference on Machine Learning (ICML), 2023
Wei Jiang
Jiayu Qin
Lingyu Wu
Changyou Chen
Tianbao Yang
Lijun Zhang
483
8
0
13 Jun 2023
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator
Learning Joint Latent Space EBM Prior Model for Multi-layer GeneratorComputer Vision and Pattern Recognition (CVPR), 2023
Jiali Cui
Ying Nian Wu
Tian Han
292
13
0
10 Jun 2023
A Semi-supervised Object Detection Algorithm for Underwater Imagery
A Semi-supervised Object Detection Algorithm for Underwater Imagery
Suraj Bijjahalli
Oscar Pizarro
S. Williams
DRL
178
3
0
07 Jun 2023
Inverse problem regularization with hierarchical variational
  autoencoders
Inverse problem regularization with hierarchical variational autoencodersIEEE International Conference on Computer Vision (ICCV), 2023
Jean Prost
Antoine Houdard
Andrés Almansa
Nicolas Papadakis
389
10
0
20 Mar 2023
eVAE: Evolutionary Variational Autoencoder
eVAE: Evolutionary Variational AutoencoderIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Zhangkai Wu
LongBing Cao
Lei Qi
BDLDRL
329
23
0
01 Jan 2023
Denoising diffusion models for out-of-distribution detection
Denoising diffusion models for out-of-distribution detection
M. Graham
W. H. Pinaya
Petru-Daniel Tudosiu
P. Nachev
Sebastien Ourselin
M. Jorge Cardoso
398
118
0
14 Nov 2022
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space
  Energy-based Model
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based ModelNeural Information Processing Systems (NeurIPS), 2022
Zhisheng Xiao
Tian Han
270
22
0
19 Sep 2022
Out-of-Distribution Detection with Class Ratio Estimation
Out-of-Distribution Detection with Class Ratio Estimation
Mingtian Zhang
Andi Zhang
Tim Z. Xiao
Yitong Sun
Jingyu Sun
OODD
234
7
0
08 Jun 2022
Top-down inference in an early visual cortex inspired hierarchical
  Variational Autoencoder
Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder
F. Csikor
B. Meszéna
Bence Szabó
Gergő Orbán
BDLDRL
294
7
0
01 Jun 2022
Diverse super-resolution with pretrained deep hiererarchical VAEs
Diverse super-resolution with pretrained deep hiererarchical VAEs
Jean Prost
Antoine Houdard
Andrés Almansa
Nicolas Papadakis
DiffM
445
0
0
20 May 2022
The Transitive Information Theory and its Application to Deep Generative
  Models
The Transitive Information Theory and its Application to Deep Generative Models
Trung Ngo Trong
Najwa Laabid
Ville Hautamaki
M. Heinäniemi
DRL
376
0
0
09 Mar 2022
Model-agnostic out-of-distribution detection using combined statistical
  tests
Model-agnostic out-of-distribution detection using combined statistical testsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Federico Bergamin
Pierre-Alexandre Mattei
Jakob Drachmann Havtorn
Hugo Senetaire
Hugo Schmutz
Lars Maaløe
Søren Hauberg
J. Frellsen
OODD
295
22
0
02 Mar 2022
Benchmarking Generative Latent Variable Models for Speech
Benchmarking Generative Latent Variable Models for Speech
Jakob Drachmann Havtorn
Lasse Borgholt
Søren Hauberg
J. Frellsen
Lars Maaløe
241
3
0
22 Feb 2022
Geometric instability of out of distribution data across autoencoder
  architecture
Geometric instability of out of distribution data across autoencoder architecture
S. Agarwala
Ben Dees
Corey Lowman
DRL
252
1
0
28 Jan 2022
Robust uncertainty estimates with out-of-distribution pseudo-inputs
  training
Robust uncertainty estimates with out-of-distribution pseudo-inputs training
Pierre Segonne
Yevgen Zainchkovskyy
Søren Hauberg
UQCVOOD
182
3
0
15 Jan 2022
When less is more: Simplifying inputs aids neural network understanding
When less is more: Simplifying inputs aids neural network understanding
R. Schirrmeister
Rosanne Liu
Sara Hooker
T. Ball
486
6
0
14 Jan 2022
Parallel Neural Local Lossless Compression
Parallel Neural Local Lossless Compression
Mingtian Zhang
James Townsend
Ning Kang
David Barber
344
7
0
13 Jan 2022
The Exponentially Tilted Gaussian Prior for Variational Autoencoders
The Exponentially Tilted Gaussian Prior for Variational Autoencoders
Griffin Floto
Stefan Kremer
Mihai Nica
DRL
238
1
0
30 Nov 2021
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Data Invariants to Understand Unsupervised Out-of-Distribution DetectionEuropean Conference on Computer Vision (ECCV), 2021
Lars Doorenbos
Raphael Sznitman
Pablo Márquez-Neila
OODD
287
7
0
26 Nov 2021
Bounds all around: training energy-based models with bidirectional
  bounds
Bounds all around: training energy-based models with bidirectional boundsNeural Information Processing Systems (NeurIPS), 2021
Cong Geng
Jia Wang
Zhiyong Gao
J. Frellsen
Søren Hauberg
390
17
0
01 Nov 2021
How to Sense the World: Leveraging Hierarchy in Multimodal Perception
  for Robust Reinforcement Learning Agents
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
Miguel Vasco
Hang Yin
Francisco S. Melo
Ana Paiva
339
9
0
07 Oct 2021
Prior and Posterior Networks: A Survey on Evidential Deep Learning
  Methods For Uncertainty Estimation
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Dennis Ulmer
Christian Hardmeier
J. Frellsen
BDLUQCVUDEDLPER
386
89
0
06 Oct 2021
Robust Out-of-Distribution Detection on Deep Probabilistic Generative
  Models
Robust Out-of-Distribution Detection on Deep Probabilistic Generative Models
Jaemoo Choi
Changyeon Yoon
Jeongwoo Bae
Myung-joo Kang
OODD
281
4
0
15 Jun 2021
Do We Really Need to Learn Representations from In-domain Data for
  Outlier Detection?
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Zhisheng Xiao
Qing Yan
Y. Amit
OODUQCV
326
21
0
19 May 2021
Conditional Image Generation by Conditioning Variational Auto-Encoders
Conditional Image Generation by Conditioning Variational Auto-EncodersInternational Conference on Learning Representations (ICLR), 2021
William Harvey
Saeid Naderiparizi
Frank Wood
BDLDRL
360
36
0
24 Feb 2021
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with
  Flow-Based Generative Models
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative ModelsIEEE Transactions on Knowledge and Data Engineering (TKDE), 2020
Yufeng Zhang
Jia Pan
Wanwei Liu
Zhenbang Chen
Jing Wang
Zhiming Liu
KenLi Li
H. Wei
OODDDRL
488
11
0
09 Feb 2020
Detection and Mitigation of Rare Subclasses in Deep Neural Network
  Classifiers
Detection and Mitigation of Rare Subclasses in Deep Neural Network ClassifiersInternational Conference on Artificial Intelligence Testing (ICAIT), 2019
Colin Paterson
R. Calinescu
Chiara Picardi
278
4
0
28 Nov 2019
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