Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection | Paper Citation Record

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This paper cites Energy-based Out-of-distribution Detection.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Energy-based Out-of-distribution Detection

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This paper cites Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data

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This paper cites RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection

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This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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This paper cites Auto-Encoding Variational Bayes.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Auto-Encoding Variational Bayes

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This paper cites NVAE: A Deep Hierarchical Variational Autoencoder.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection NVAE: A Deep Hierarchical Variational Autoencoder

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Ladder Variational Autoencoders

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This paper cites Hierarchical VAEs Know What They Don't Know.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Hierarchical VAEs Know What They Don't Know

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This paper cites Likelihood Ratios for Out-of-Distribution Detection.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Likelihood Ratios for Out-of-Distribution Detection

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This paper cites Do Deep Generative Models Know What They Don't Know?.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Do Deep Generative Models Know What They Don't Know?

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Generating Sentences from a Continuous Space

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Improving Variational Inference with Inverse Autoregressive Flow

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Variational Lossy Autoencoder

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Avoiding Latent Variable Collapse With Generative Skip Models

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Large Scale GAN Training for High Fidelity Natural Image Synthesis

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Denoising Diffusion Probabilistic Models

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This paper cites BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

Reference 18

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This paper cites Consistency Regularization for Variational Auto-Encoders.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Consistency Regularization for Variational Auto-Encoders

Reference 19

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Rate-Regularization and Generalization in VAEs

Reference 20

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Learning Autoencoders with Relational Regularization

Reference 21

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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This paper cites Input complexity and out-of-distribution detection with likelihood-based generative models.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Input complexity and out-of-distribution detection with likelihood-based generative models

Reference 24

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Further Analysis of Outlier Detection with Deep Generative Models

Reference 25

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

Reference 26

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Deep Residual Learning for Image Recognition

Reference 27

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Krizhevsky, I

Reference 28

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Lecun, Y

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Dimensionality compression and expansion in Deep Neural Networks

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection An exact mapping between the Variational Renormalization Group and Deep Learning

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection The information bottleneck method

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Shamir, S

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Deep Variational Information Bottleneck

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Deep Semi-Supervised Anomaly Detection

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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This paper cites Deng, The mnist database of handwritten digit images for machine learning research, IEEE Signal Processing Magazine 29 (6) (2012) 141–142.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Deng, The mnist database of handwritten digit images for machine learning research, IEEE Signal Processing Magazine 29 (6) (2012) 141–142

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This paper cites Krizhevsky, Learning multiple layers of features from tiny images, Techni- cal Report, University of Toronto (2009) 32–33.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Krizhevsky, Learning multiple layers of features from tiny images, Techni- cal Report, University of Toronto (2009) 32–33

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Netzer, T

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Bulatov, notMNIST Dataset, Available at http://yaroslavvb

Reference 42

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Deep Learning and the Information Bottleneck Principle

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Importance Weighted Autoencoders

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Adam: A Method for Stochastic Optimization

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This paper cites Given the total latent budget b and number of lay- ers N , the latent dimension li for layer i is: li = b · (1 − r) · ri−1 1 − rN.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Given the total latent budget b and number of lay- ers N , the latent dimension li for layer i is: li = b · (1 − r) · ri−1 1 − rN

Reference 48

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

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This paper cites Training time for a single HV AE model on one Nvidia GTX 2080 Ti GPU was approximately 48 hours for grayscale images and > 200 hours for natural images.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Training time for a single HV AE model on one Nvidia GTX 2080 Ti GPU was approximately 48 hours for grayscale images and > 200 hours for natural images

Reference 52

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Observation d9dde2e8-28d7-4701-b9f9-e48da7e78d47 · outbound

This paper cites an unresolved cited work.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T04:42:38.054011Z

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Observation 1f48dc9d-06b2-43be-90bf-3610799f7bb7 · outbound

This paper cites an unresolved cited work.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:42:38.652296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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